Author name cluster

Giorgio Metta

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54 papers

2 author rows

IROS Conference 2020 Conference Paper

Non-Linear Trajectory Optimization for Large Step-Ups: Application to the Humanoid Robot Atlas

Stefano Dafarra
Sylvain Bertrand
Robert J. Griffin
Giorgio Metta
Daniele Pucci
Jerry E. Pratt

Performing large step-ups is a challenging task for a humanoid robot. It requires the robot to perform motions at the limit of its reachable workspace while straining to move its body upon the obstacle. This paper presents a non-linear trajectory optimization method for generating step-up motions. We adopt a simplified model of the centroidal dynamics to generate feasible Center of Mass trajectories aimed at reducing the torques required for the step-up motion. The activation and deactivation of contacts at both feet are considered explicitly. The output of the planner is a Center of Mass trajectory plus an optimal duration for each walking phase. These desired values are stabilized by a whole-body controller that determines a set of desired joint torques. We experimentally demonstrate that by using trajectory optimization techniques, the maximum torque required to the full-size humanoid robot Atlas can be reduced up to 20% when performing a step-up motion.

ICRA Conference 2020 Conference Paper

Whole-Body Walking Generation using Contact Parametrization: A Non-Linear Trajectory Optimization Approach

Stefano Dafarra
Giulio Romualdi
Giorgio Metta
Daniele Pucci

In this paper, we describe a planner capable of generating walking trajectories by using the centroidal dynamics and the full kinematics of a humanoid robot model. The interaction between the robot and the walking surface is modeled explicitly through a novel contact parametrization. The approach is complementarity-free and does not need a predefined contact sequence. By solving an optimal control problem we obtain walking trajectories. In particular, through a set of constraints and dynamic equations, we model the robot in contact with the ground. We describe the objective the robot needs to achieve with a set of tasks. The whole optimal control problem is transcribed into an optimization problem via a Direct Multiple Shooting approach and solved with an off-the-shelf solver. We show that it is possible to achieve walking motions automatically by specifying a minimal set of references, such as a constant desired Center of Mass velocity and a reference point on the ground.

IROS Conference 2019 Conference Paper

Closed-loop Force Control of a Pneumatic Gripper Actuated by Two Pressure Regulators

Rocco Antonio Romeo
Luca Fiorio
Edwin Johnatan Avila Mireles
Ferdinando Cannella
Giorgio Metta
Daniele Pucci

Robotic arms can perform grasping actions thanks to their “dexteorus” part, i. e. the gripper. Among the various categories, nowadays pneumatic grippers became the most employed in industry, as they have low cost and little bulkiness. Despite their simplicity, controlling the force applied by these grippers is not straightforward due to the dependence of such a force on the air pressure in the gripper chambers. As a result, it is still tricky to implement closed-loop force control for pneumatic grippers. This paper intends to deliver a control scheme relying on the force measurement to control pneumatic grippers. The force might be measured through a commercial sensor (e. g. a load cell) and fed back to close the control loop. This includes a calibration which maps the force-pressure relation taking into account both desired force and length of the gripper fingers. The control scheme exploits two different pressure regulators to precisely adjust the air pressure inside the gripper chambers (i. e. opening and closing chambers). To this aim, a quadratic programming algorithm is employed. The control scheme performance revealed to be good: results will be shown in terms of gripper response to sinusoidal and step inputs, along with the pressure-force characterization.

IROS Conference 2018 Conference Paper

A Control Architecture with Online Predictive Planning for Position and Torque Controlled Walking of Humanoid Robots

Stefano Dafarra
Gabriele Nava
Marie Charbonneau
Nuno Guedelha
Francisco Andrade 0002
Silvio Traversaro
Luca Fiorio
Francesco Romano

A common approach to the generation of walking patterns for humanoid robots consists in adopting a layered control architecture. This paper proposes an architecture composed of three nested control loops. The outer loop exploits a robot kinematic model to plan the footstep positions. In the mid layer, a predictive controller generates a Center of Mass trajectory according to the well-known table-cart model. Through a whole-body inverse kinematics algorithm, we can define joint references for position controlled walking. The outcomes of these two loops are then interpreted as inputs of a stack-of-task QP-based torque controller, which represents the inner loop of the presented control architecture. This resulting architecture allows the robot to walk also in torque control, guaranteeing higher level of compliance. Real world experiments have been carried on the humanoid robot iCub.

IROS Conference 2018 Conference Paper

Neuroscientifically-Grounded Research for Improved Human-Robot Interaction

Kyveli Kompatsiari
Jairo Pérez-Osorio
Davide De Tommaso
Giorgio Metta
Agnieszka Wykowska

The present study highlights the benefits of using well-controlled experimental designs, grounded in experimental psychology research and objective neuroscientific methods, for generating progress in human-robot interaction (HRI) research. More specifically, we aimed at implementing a well-studied paradigm of attentional cueing through gaze (the so-called “joint attention” or “gaze cueing”) in an HRI protocol involving the iCub robot. Similarly to documented results in gaze-cueing research, we found faster response times and enhanced event-related potentials of the EEG signal for discrimination of cued, relative to uncued, targets. These results are informative for the robotics community by showing that a humanoid robot with mechanistic eyes and human-like characteristics of the face is in fact capable of engaging a human in joint attention to a similar extent as another human would do. More generally, we propose that the methodology of combining neuroscience methods with an HRI protocol, contributes to understanding mechanisms of human social cognition in interactions with robots and to improving robot design, thanks to systematic and well-controlled experimentation tapping onto specific cognitive mechanisms of the human, such as joint attention.

IROS Conference 2018 Conference Paper

Transferring Visuomotor Learning from Simulation to the Real World for Robotics Manipulation Tasks

Phuong D. H. Nguyen
Tobias Fischer 0001
Hyung Jin Chang
Ugo Pattacini
Giorgio Metta
Yiannis Demiris

Hand-eye coordination is a requirement for many manipulation tasks including grasping and reaching. However, accurate hand-eye coordination has shown to be especially difficult to achieve in complex robots like the iCub humanoid. In this work, we solve the hand-eye coordination task using a visuomotor deep neural network predictor that estimates the arm's joint configuration given a stereo image pair of the arm and the underlying head configuration. As there are various unavoidable sources of sensing error on the physical robot, we train the predictor on images obtained from simulation. The images from simulation were modified to look realistic using an image-to-image translation approach. In various experiments, we first show that the visuomotor predictor provides accurate joint estimates of the iCub's hand in simulation. We then show that the predictor can be used to obtain the systematic error of the robot's joint measurements on the physical iCub robot. We demonstrate that a calibrator can be designed to automatically compensate this error. Finally, we validate that this enables accurate reaching of objects while circumventing manual fine-calibration of the robot.

IROS Conference 2017 Conference Paper

A parallel kinematic mechanism for the torso of a humanoid robot: Design, construction and validation

Luca Fiorio
Alessandro Scalzo
Lorenzo Natale
Giorgio Metta
Alberto Parmiggiani

The torso of a humanoid robot is a fundamental part of its kinematic structure because it defines the reachable workspace, supports the entire upper-body and can be used to control the position of the center of mass. The majority of the torso joints are designed exploiting serial or differential mechanisms, while parallel kinematic structures are less used mainly because of their greater design complexity. This paper describes the design and construction of a 4 degrees of freedom (DoF) torso for our new humanoid robot. Three degrees of freedom, namely roll, pitch and heave, have been implemented using a 3 DoF parallel kinematic structure, while the fourth DoF, namely yaw, has been implemented with a rotational joint on top of the parallel structure. The design has been optimized to reduce the cost and the volume of the system. A first prototype of the torso has been constructed and validated with respect to our design requirements. Eventually, experimental tests have been conducted to assess the functionality of the proposed system.

ICRA Conference 2017 Conference Paper

Incremental robot learning of new objects with fixed update time

Raffaello Camoriano
Giulia Pasquale
Carlo Ciliberto
Lorenzo Natale
Lorenzo Rosasco
Giorgio Metta

We consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment. We propose an incremental variant of the Regularized Least Squares for Classification (RLSC) algorithm, and exploit its structure to seamlessly add new classes to the learned model. The presented algorithm addresses the problem of having an unbalanced proportion of training examples per class, which occurs when new objects are presented to the system for the first time. We evaluate our algorithm on both a machine learning benchmark dataset and two challenging object recognition tasks in a robotic setting. Empirical evidence shows that our approach achieves comparable or higher classification performance than its batch counterpart when classes are unbalanced, while being significantly faster.

ICRA Conference 2017 Conference Paper

Self-supervised learning of tool affordances from 3D tool representation through parallel SOM mapping

Tanis Mar
Vadim Tikhanoff
Giorgio Metta
Lorenzo Natale

Future humanoid robots will be expected to carry out a wide range of tasks for which they had not been originally equipped by learning new skills and adapting to their environment. A crucial requirement towards that goal is to be able to take advantage of external elements as tools to perform tasks for which their own manipulators are insufficient; the ability to autonomously learn how to use tools will render robots far more versatile and simpler to design. Motivated by this prospect, this paper proposes and evaluates an approach to allow robots to learn tool affordances based on their 3D geometry. To this end, we apply tool-pose descriptors to represent tools combined with the way in which they are grasped, and affordance vectors to represent the effect tool-poses achieve in function of the action performed. This way, tool affordance learning consists in determining the mapping between these 2 representations, which is achieved in 2 steps. First, the dimensionality of both representations is reduced by unsupervisedly mapping them onto respective Self-Organizing Maps (SOMs). Then, the mapping between the neurons in the tool-pose SOM and the neurons in the affordance SOM for pairs of tool-poses and their corresponding affordance vectors, respectively, is learned with a neural based regression model. This method enables the robot to accurately predict the effect of its actions using tools, and thus to select the best action for a given goal, even with tools not seen on the learning phase.

IROS Conference 2017 Conference Paper

The design and validation of the R1 personal humanoid

Alberto Parmiggiani
Luca Fiorio
Alessandro Scalzo
Anand Vazhapilli Sureshbabu
Marco Randazzo
Marco Maggiali
Ugo Pattacini
Hagen Lehmann

In recent years the robotics field has witnessed an interesting new trend. Several companies started the production of service robots whose aim is to cooperate with humans. The robots developed so far are either rather expensive or unsuitable for manipulation tasks. This article presents the result of a project which wishes to demonstrate the feasibility of an affordable humanoid robot. R1 is able to navigate, and interact with the environment (grasping and carrying objects, operating switches, opening doors etc). The robot is also equipped with a speaker, microphones and it mounts a display in the head to support interaction using natural channels like speech or (simulated) eye movements. The final cost of the robot is expected to range around that of a family car, possibly, when produced in large quantities, even significantly lower. This goal was tackled along three synergistic directions: use of polymeric materials, light-weight design and implementation of novel actuation solutions. These lines, as well as the robot with its main features, are described hereafter.

ICRA Conference 2016 Conference Paper

Incremental semiparametric inverse dynamics learning

Raffaello Camoriano
Silvio Traversaro
Lorenzo Rosasco
Giorgio Metta
Francesco Nori

This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. The result is an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.

IROS Conference 2015 Conference Paper

A best-effort approach for run-time channel prioritization in real-time robotic application

Ali Paikan
Ugo Pattacini
Daniele E. Domenichelli
Marco Randazzo
Giorgio Metta
Lorenzo Natale

Application domains of robotic systems are growing in complexity. It seems therefore plausible that robotic software will continue to be designed to be executed on distributed computer architectures interconnected through a network. It is a common practice today to rely on best-effort performance and assume that the latter are adequate given enough computational and networking resources. This approach however does not make best use of the available resources and, maybe more importantly, does not guarantee that performance remain constant over time. Real-time and Quality of Service become therefore important aspects in the software architecture of a robot. This article describes an approach for introducing these concepts in a publish-subscribe software middleware. The key contribution of our approach is that it leverages on the services provided by the operating system (scheduling priority and packet QoS) and abstracts them in a set of levels of priority that can be assigned dynamically, and with the granularity of individual communication channels. We implemented our approach on the YARP middleware and performed an experimental evaluation that demonstrates its benefit for increasing determinism and reducing latency in data communication. We further demonstrate this in a real-robot experiment that shows increased performance in a closed-loop scenario.

IROS Conference 2015 Conference Paper

A new design of a fingertip for the iCub hand

Nawid Jamali
Marco Maggiali
Francesco Giovannini
Giorgio Metta
Lorenzo Natale

Tactile sensing is of fundamental importance for object manipulation and perception. Several sensors for hands have been proposed in the literature, however, only a few of them can be fully integrated with robotic hands. Typical problems preventing integration include the need for deformable sensors that can be deployed on curved surfaces, and wiring complexity. In this paper we describe a fingertip for the hands of the iCub robot, each fingertip consists of 12 sensors. Our approach builds on previous work on the iCub tactile system. The sensing elements of the fingertip are capacitive sensors made from a flexible PCB, and a multi-layer fabric that includes the dielectric material and the conductive layer. The novelty the proposed sensor lies in incorporating the multi-layer fabric technology into a small fingertip sensor that can be attached to the hands of a humanoid robot. The new sensors are more robust. The manufacturing is easier and relies on industrial techniques for the fabrication of the components, which results in higher repeatability. We performed experimental characterization of the sensor. We show that the sensor is able to detect forces as low as 0. 05 N with no cross-talk between the taxels. We identified some hysteresis in the response of the sensor which must be taken into account if the robot exerts large forces for a long period of time. The taxels have spatially overlapping receptive fields, this has been demonstrated to be a useful property that allows hyperacuity.

IROS Conference 2015 Conference Paper

Learning peripersonal space representation through artificial skin for avoidance and reaching with whole body surface

Alessandro Roncone
Matej Hoffmann
Ugo Pattacini
Giorgio Metta

With robots leaving factory environments and entering less controlled domains, possibly sharing living space with humans, safety needs to be guaranteed. To this end, some form of awareness of their body surface and the space surrounding it is desirable. In this work, we present a unique method that lets a robot learn a distributed representation of space around its body (or peripersonal space) by exploiting a whole-body artificial skin and through physical contact with the environment. Every taxel (tactile element) has a visual receptive field anchored to it. Starting from an initially blank state, the distance of every object entering this receptive field is visually perceived and recorded, together with information whether the object has eventually contacted the particular skin area or not. This gives rise to a set of probabilities that are updated incrementally and that carry information about the likelihood of particular events in the environment contacting a particular set of taxels. The learned representation naturally serves the purpose of predicting contacts with the whole body of the robot, which is of clear behavioral relevance. Furthermore, we devised a simple avoidance controller that is triggered by this representation, thus endowing a robot with a “margin of safety” around its body. Finally, simply reversing the sign in the controller we used gives rise to simple “reaching” for objects in the robot's vicinity, which automatically proceeds with the most activated (closest) body part.

ICRA Conference 2015 Conference Paper

Self-supervised learning of grasp dependent tool affordances on the iCub Humanoid robot

Tanis Mar
Vadim Tikhanoff
Giorgio Metta
Lorenzo Natale

The ability to learn about and efficiently use tools constitutes a desirable property for general purpose humanoid robots, as it allows them to extend their capabilities beyond the limitations of their own body. Yet, it is a topic that has only recently been tackled from the robotics community. Most of the studies published so far make use of tool representations that allow their models to generalize the knowledge among similar tools in a very limited way. Moreover, most studies assume that the tool is always grasped in its common or canonical grasp position, thus not considering the influence of the grasp configuration in the outcome of the actions performed with them. In the current paper we present a method that tackles both issues simultaneously by using an extended set of functional features and a novel representation of the effect of the tool use. Together, they implicitly account for the grasping configuration and allow the iCub to generalize among tools based on their geometry. Moreover, learning happens in a self-supervised manner: First, the robot autonomously discovers the affordance categories of the tools by clustering the effect of their usage. These categories are subsequently used as a teaching signal to associate visually obtained functional features to the expected tool's affordance. In the experiments, we show how this technique can be effectively used to select, given a tool, the best action to achieve a desired effect.

IROS Conference 2014 Conference Paper

An alternative approach to robot safety

Alberto Parmiggiani
Marco Randazzo
Lorenzo Natale
Giorgio Metta

Robotic technology has made significant progresses in the past years. Robots are now common in large manufacturing plants and other industrial settings, safely confined in closed work cells. But to be even more helpful, robots need the capability of interacting physically with humans, and with unstructured environments. This poses new challenges in the design of safe robotic systems. In this article we addressed this problem by proposing a novel design for the joints of the iCub robot. The new design provides the robot with an overload protection mechanism. The overload protection acts as a “passive” torque saturator, which is intrinsically safe. We constructed a prototype of a robotic joint that implements this approach. We first show that our solution is effective in a typical impact scenario. We then evaluate the possible problems arising when the device is controlled with a position control loop. We show that a conventional feedback control loop can trigger positive feedback and instability. Operating the actuator in these conditions is dangerous and can lead to severe failures. We therefore propose the implementation of a relatively simple control strategy that allows to avoid this situation by monitoring slippage, without additional sensors. The quantitative evaluations in the paper demonstrate that our approach is effective and can improve the robustness and safety of complex robotic systems. Indeed these aspects are particularly critical in the case of humaniod robots that are systems prone to severe whole-body impacts in unstructured environments (e. g. falling).

ICRA Conference 2014 Conference Paper

Automatic kinematic chain calibration using artificial skin: Self-touch in the iCub humanoid robot

Alessandro Roncone
Matej Hoffmann
Ugo Pattacini
Giorgio Metta

Calibration continues to receive significant attention in robotics because of its key impact on performance and cost associated with the operation of complex robots. Calibration of kinematic parameters is typically the first mandatory step. To this end, a variety of metrology systems and corresponding algorithms have been described in the literature relying on measurements of the pose of the end-effector using a camera or laser tracking system, or, exploiting constraints arising from contacts of the end-effector with the environment. In this work, we take inspiration from the behavior of infants and certain animals, who are believed to use self-stimulation or self-touch to “calibrate” their body representations, and present a new solution to this problem by letting the robot close the kinematic chain by touching its own body. The robot considered in this paper is sensorized with tactile arrays for a total of about 4200 sensing points. The correspondence between the predicted contact point from existing forward kinematics and the actual position on the robot's `skin' provides sample data that allows refining the kinematic representation (DH parameters). The data collection procedure is automated - self-touch is autonomously executed by the robot - and can be repeated at any time, providing a compact self-calibration system that does not require an external measurement apparatus.

IROS Conference 2014 Conference Paper

Enhancing software module reusability using port plug-ins: An experiment with the iCub robot

Ali Paikan
Vadim Tikhanoff
Giorgio Metta
Lorenzo Natale

Systematically developing high-quality reusable software components is a difficult task and requires careful design to find a proper balance between potential reuse, functionalities and ease of implementation. Extendibility is an important property for software which helps to reduce cost of development and significantly boosts its reusability. This work introduces an approach to enhance components reusability by extending their functionalities using plug-ins at the level of the connection points (ports). Application-dependent functionalities such as data monitoring and arbitration can be implemented using a conventional scripting language and plugged into the ports of components. The main advantage of our approach is that it avoids to introduce application-dependent modifications to existing components, thus reducing development time and fostering the development of simpler and therefore more reusable components. Another advantage of our approach is that it reduces communication and deployment overheads as extra functionalities can be added without introducing additional modules. The details of the plug-in system is described in the paper and its advantages for the development of robotics applications are demonstrated by developing a step-by-step example on the iCub humanoid robot.

IROS Conference 2014 Conference Paper

Exploiting global force torque measurements for local compliance estimation in tactile arrays

Carlo Ciliberto
Luca Fiorio
Marco Maggiali
Lorenzo Natale
Lorenzo Rosasco
Giorgio Metta
Giulio Sandini
Francesco Nori

In this paper we tackle the problem of estimating the local compliance of tactile arrays exploiting global measurements from a single force and torque sensor. The proposed procedure exploits a transformation matrix (describing the relative position between the local tactile elements and the global force/torque measurements) to define a linear regression problem on the unknown local stiffness. Experiments have been conducted on the foot of the iCub robot, sensorized with a single force/torque sensor and a tactile array of 250 tactile elements (taxels) on the foot sole. Results show that a simple calibration procedure can be employed to estimate the stiffness parameters of virtual springs over a tactile array and to use these model to predict normal forces exerted on the array based only on the tactile feedback. Leveraging on previous works [1] the proposed procedure does not necessarily need a-priori information on the transformation matrix of the taxels which can be directly estimated from available measurements.

IROS Conference 2014 Conference Paper

Partial force control of constrained floating-base robots

Andrea Del Prete
Nicolas Mansard
Francesco Nori
Giorgio Metta
Lorenzo Natale

Legged robots are typically in rigid contact with the environment at multiple locations, which add a degree of complexity to their control. We present a method to control the motion and a subset of the contact forces of a floating-base robot. We derive a new formulation of the lexicographic optimization problem typically arising in multi-task motion/force control frameworks. The structure of the constraints of the problem (i. e. the dynamics of the robot) allows us to find a sparse analytical solution. This leads to an equivalent optimization with reduced computational complexity, comparable to inverse-dynamics based approaches. At the same time, our method preserves the flexibility of optimization based control frameworks. Simulations were carried out to achieve different multi-contact behaviors on a 23-degree-of-freedom humanoid robot, validating the presented approach. A comparison with another state-of-the-art control technique with similar computational complexity shows the benefits of our controller, which can eliminate force/torque discontinuities.

ICRA Conference 2014 Conference Paper

Prioritized optimal control

Andrea Del Prete
Francesco Romano
Lorenzo Natale
Giorgio Metta
Giulio Sandini
Francesco Nori

This paper presents a new technique to control highly redundant mechanical systems, such as humanoid robots. We take inspiration from two approaches. Prioritized control is a widespread multi-task technique in robotics and animation: tasks have strict priorities and they are satisfied only as long as they do not conflict with any higher-priority task. Optimal control instead formulates an optimization problem whose solution is either a feedback control policy or a feedforward trajectory of control inputs. We introduce strict priorities in multi-task optimal control problems, as an alternative to weighting task errors proportionally to their importance. This ensures the respect of the specified priorities, while avoiding numerical conditioning issues. We compared our approach with both prioritized control and optimal control with tests on a simulated robot with 11 degrees of freedom.

ICRA Conference 2014 Conference Paper

Three-finger precision grasp on incomplete 3D point clouds

Ilaria Gori
Ugo Pattacini
Vadim Tikhanoff
Giorgio Metta

We present a novel method for three-finger precision grasp and its implementation in a complete grasping tool-chain. We start from binocular vision to recover the partial 3D structure of unknown objects. We then process the incomplete 3D point clouds searching for good triplets according to a function that accounts for both the feasibility and the stability of the solution. In particular, while stability is determined using the classical force-closure approach, feasibility is evaluated according to a new measure that includes information about the possible configuration shapes of the hand as well as the hand's inverse kinematics. We finally extensively assess the proposed method using the stereo vision and the kinematics of the iCub robot.

IROS Conference 2013 Conference Paper

Cooperative human robot interaction systems: IV. Communication of shared plans with Naïve humans using gaze and speech

Stéphane Lallée
Katharina Hamann
Jasmin Steinwender
Felix Warneken
Uriel Martinez-Hernandez
Hector Barron-Gonzalez
Ugo Pattacini
Ilaria Gori

Cooperation 1 is at the core of human social life. In this context, two major challenges face research on humanrobot interaction: the first is to understand the underlying structure of cooperation, and the second is to build, based on this understanding, artificial agents that can successfully and safely interact with humans. Here we take a psychologically grounded and human-centered approach that addresses these two challenges. We test the hypothesis that optimal cooperation between a naïve human and a robot requires that the robot can acquire and execute a joint plan, and that it communicates this joint plan through ecologically valid modalities including spoken language, gesture and gaze. We developed a cognitive system that comprises the human-like control of social actions, the ability to acquire and express shared plans and a spoken language stage. In order to test the psychological validity of our approach we tested 12 naïve subjects in a cooperative task with the robot. We experimentally manipulated the presence of a joint plan (vs. a solo plan), the use of task-oriented gaze and gestures, and the use of language accompanying the unfolding plan. The quality of cooperation was analyzed in terms of proper turn taking, collisions and cognitive errors. Results showed that while successful turn taking could take place in the absence of the explicit use of a joint plan, its presence yielded significantly greater success. One advantage of the solo plan was that the robot would always be ready to generate actions, and could thus adapt if the human intervened at the wrong time, whereas in the joint plan the robot expected the human to take his/her turn. Interestingly, when the robot represented the action as involving a joint plan, gaze provided a highly potent nonverbal cue that facilitated successful collaboration and reduced errors in the absence of verbal communication. These results support the cooperative stance in human social cognition, and suggest that cooperative robots should employ joint plans, fully communicate them in order to sustain effective collaboration while being ready to adapt if the human makes a midstream mistake.

ICRA Conference 2013 Conference Paper

Developmental action perception for manipulative interaction

Ryo Saegusa
Giorgio Metta
Giulio Sandini
Lorenzo Natale

The paper describes a developmental framework of action-driven perception in anthropomorphic robots. The key idea of the framework is that action develops the agent's perception of the own body and its action. In this framework, a robot voluntarily generates movements, and then develops the ability to perceive its own body and the effects of action primitives. The robot, moreover, demonstrates manipulative actions composed of the learned primitives, and characterizes the actions based on their sensory effects. After learning, the robot can predictively recognize humans' manipulative actions with cross-modal recovery of unavailable sensory information and reproduce the recognized actions. We evaluated the proposed framework in experiments with a real robot. In the experiments, we achieved developmental recognition of human actions as well as their reproduction.

IROS Conference 2013 Conference Paper

Ensuring safety of policies learned by reinforcement: Reaching objects in the presence of obstacles with the iCub

Shashank Pathak
Luca Pulina
Giorgio Metta
Armando Tacchella

Given a stochastic policy learned by reinforcement, we wish to ensure that it can be deployed on a robot with demonstrably low probability of unsafe behavior. Our case study is about learning to reach target objects positioned close to obstacles, and ensuring a reasonably low collision probability. Learning is carried out in a simulator to avoid physical damage in the trial-and-error phase. Once a policy is learned, we analyze it with probabilistic model checking tools to identify and correct potential unsafe behaviors. The whole process is automated and, in principle, it can be integrated step-by-step with routine task-learning. As our results demonstrate, automated fixing of policies is both feasible and highly effective in bounding the probability of unsafe behaviors.

JMLR Journal 2013 Journal Article

Keep It Simple And Sparse: Real-Time Action Recognition

Sean Ryan Fanello
Ilaria Gori
Giorgio Metta
Francesca Odone

Sparsity has been showed to be one of the most important properties for visual recognition purposes. In this paper we show that sparse representation plays a fundamental role in achieving one-shot learning and real-time recognition of actions. We start off from RGBD images, combine motion and appearance cues and extract state-of-the-art features in a computationally efficient way. The proposed method relies on descriptors based on 3D Histograms of Scene Flow (3DHOFs) and Global Histograms of Oriented Gradient (GHOGs); adaptive sparse coding is applied to capture high-level patterns from data. We then propose a simultaneous on-line video segmentation and recognition of actions using linear SVMs. The main contribution of the paper is an effective real-time system for one-shot action modeling and recognition; the paper highlights the effectiveness of sparse coding techniques to represent 3D actions. We obtain very good results on three different data sets: a benchmark data set for one-shot action learning (the ChaLearn Gesture Data Set), an in-house data set acquired by a Kinect sensor including complex actions and gestures differing by small details, and a data set created for human-robot interaction purposes. Finally we demonstrate that our system is effective also in a human-robot interaction setting and propose a memory game, âAll Gestures You Canâ, to be played against a humanoid robot. [abs] [ pdf ][ bib ] &copy JMLR 2013. ( edit, beta )

IROS Conference 2013 Conference Paper

Model of cyclotorsion in a tendon driven eyeball: Theoretical model and qualitative evaluation on a robotic platform

Francesco Nori
Giulio Sandini
Giorgio Metta

In this paper we describe the mathematical model of a tendon driven eye. We focus on describing its movements, posing a specific attention on cyclotorsion, that is the rotation around the eye optical axis. This study aims at understanding the cause of a cyclotorsion effect that has been noticed on a real setup. The paper starts from a qualitative analysis of the effect. Then, it proposes two different models for its motion. Finally both models are validated by comparing their predictions with the outcomes on the real robot. Given the complexity of the system and of its motion results are for the moment just qualitative and quantitative comparison will be the goal of our future works.

IROS Conference 2013 Conference Paper

On the impact of learning hierarchical representations for visual recognition in robotics

Carlo Ciliberto
Sean Ryan Fanello
Matteo Santoro
Lorenzo Natale
Giorgio Metta
Lorenzo Rosasco

Recent developments in learning sophisticated, hierarchical image representations have led to remarkable progress in the context of visual recognition. While these methods are becoming standard in modern computer vision systems, they are rarely adopted in robotics. The question arises of whether solutions, which have been primarily developed for image retrieval, can perform well in more dynamic and unstructured scenarios. In this paper we tackle this question performing an extensive evaluation of state of the art methods for visual recognition on a iCub robot. We consider the problem of classifying 15 different objects shown by a human demonstrator in a challenging Human-Robot Interaction scenario. The classification performance of hierarchical learning approaches are shown to outperform benchmark solutions based on local descriptors and template matching. Our results show that hierarchical learning systems are computationally efficient and can be used for real-time training and recognition of objects.

ICRA Conference 2013 Conference Paper

Weakly supervised strategies for natural object recognition in robotics

Sean Ryan Fanello
Carlo Ciliberto
Lorenzo Natale
Giorgio Metta

The paper aims at building a computer vision system for automatic image labeling in robotics scenarios. We show that the weak supervision provided by a human demonstrator, through the exploitation of the independent motion, enables a realistic Human-Robot Interaction (HRI) and achieves an automatic image labeling. We start by reviewing the underlying principles of our previous method for egomotion compensation [1], then we extend our approach removing the dependency on a known kinematics in order to provide a general method for a wide range of devices. From sparse salient features we predict the egomotion of the camera through a heteroscedastic learning method. Subsequently we use an object recognition framework for testing the automatic image labeling process: we rely on the State of the Art method from Yang et al. [2], employing local features augmented through a sparse coding stage and classified with linear SVMs. The application has been implemented and validated on the iCub humanoid robot and experiments are presented to show the effectiveness of the proposed approach. The contribution of the paper is twofold: first we overcome the dependency on the kinematics in the independent motion detection method, secondly we present a practical application for automatic image labeling through a natural HRI.

IROS Conference 2012 Conference Paper

A heteroscedastic approach to independent motion detection for actuated visual sensors

Carlo Ciliberto
Sean Ryan Fanello
Lorenzo Natale
Giorgio Metta

We present an original method for independent motion detection in dynamic scenes. The algorithm is designed for robotics real-time applications and it overcomes the short-comings of current approaches for the egomotion estimation in presence of many outliers, occlusions and cluttered background. The method relies on a stereo system which performs the reprojection of a sparse set of features following the camera displacement. We assume that noisy prior knowledge of the motion is available (i. e. a robot's kinematic model). Since this estimation leads to a heteroscedastic regression problem due to input-dependent noise, we employ a simple, but computationally efficient approach in order to accurately determine the latent egomotion subspace spanned by the Degrees of Freedom (DOFs) of the robot. The algorithm has been implemented and validated on the iCub humanoid robot. Qualitative and quantitative experiments are presented to show the effectiveness of the proposed approach. The contribution of the paper is a modular framework for independent motion detection naturally extendable to any architecture featuring a visual sensor that can be directly controllable.

ICRA Conference 2012 Conference Paper

Active object recognition on a humanoid robot

Björn Browatzki
Vadim Tikhanoff
Giorgio Metta
Heinrich H. Bülthoff
Christian Wallraven

Interaction with its environment is a key requisite for a humanoid robot. Especially the ability to recognize and manipulate unknown objects is crucial to successfully work in natural environments. Visual object recognition, however, still remains a challenging problem, as three-dimensional objects often give rise to ambiguous, two-dimensional views. Here, we propose a perception-driven, multisensory exploration and recognition scheme to actively resolve ambiguities that emerge at certain viewpoints. We define an efficient method to acquire two-dimensional views in an object-centered task space and sample characteristic views on a view sphere. Information is accumulated during the recognition process and used to select actions expected to be most beneficial in discriminating similar objects. Besides visual information we take into account proprioceptive information to create more reliable hypotheses. Simulation and real-world results clearly demonstrate the efficiency of active, multisensory exploration over passive, visiononly recognition methods.

IROS Conference 2012 Conference Paper

Control of contact forces: The role of tactile feedback for contact localization

Andrea Del Prete
Francesco Nori
Giorgio Metta
Lorenzo Natale

This paper investigates the role of precise estimation of contact points in force control. This analysis is motivated by scenarios in which robots make contacts, either voluntarily or accidentally, with different parts of their body. Control paradigms that are usually implemented in robots with no tactile system, make the hypothesis that contacts occur at the end-effectors only. In this paper we try to investigate what happens when this assumption is not verified. First we consider a simple feedforward force control law, and then we extend it by introducing a proportional feedback term. For both controllers we find the error in the resulting contact force, that is induced by a hypothetic error in the estimation of the contact point. We show that, depending on the geometry of the contact, incorrect estimation of contact points can induce undesired joint accelerations. We validate the presented analysis with tests on a simulated robot arm. Moreover we consider a complex real world scenario, where most of the assumptions that we make in our analytical derivation do not hold. Through tests on the iCub humanoid robot we see how errors in contact localization affect the performance of a parallel force/position controller. In order to estimate contact points and contact forces on the forearm of the iCub we do not use any model of the environment, but we exploit its 6-axis force/torque sensor and its sensorized skin.

IROS Conference 2012 Conference Paper

Embodied hyperacuity from Bayesian perception: Shape and position discrimination with an iCub fingertip sensor

Nathan F. Lepora
Uriel Martinez-Hernandez
Hector Barron-Gonzalez
Mathew H. Evans
Giorgio Metta
Tony J. Prescott

Recent advances in modeling animal perception has motivated an approach of Bayesian perception applied to biomimetic robots. This study presents an initial application of Bayesian perception on an iCub fingertip sensor mounted on a dedicated positioning robot. We systematically probed the test system with five cylindrical stimuli offset by a range of positions relative to the fingertip. Testing the real-time speed and accuracy of shape and position discrimination, we achieved sub-millimeter accuracy with just a few taps. This result is apparently the first explicit demonstration of perceptual hyperacuity in robot touch, in that object positions are perceived more accurately than the taxel spacing. We also found substantial performance gains when the fingertip can reposition itself to avoid poor perceptual locations, which indicates that improved robot perception could mimic active perception in animals.

IROS Conference 2011 Conference Paper

A comparison between joint level torque sensing and proximal F/T sensor torque estimation: Implementation on the iCub

Marco Randazzo
Matteo Fumagalli 0001
Francesco Nori
Lorenzo Natale
Giorgio Metta
Giulio Sandini

When a robot is required to safely interact with a physical environment, two approaches are typically reported in literature: using a force/torque sensor to regulate the interaction forces at the end effector, or integrating sensors in each robot joint to regulate their torques. In this paper we want to discuss the benefits and the disadvantages of the two approaches, showing a direct comparison between the information which can be obtained from the two categories of sensors. Results obtained on the new iCub arm, which integrates torque sensing capabilities at joint level will be presented and discussed.

ICRA Conference 2011 Conference Paper

Biologically-inspired time and location of impact prediction from optical flow

Chris McCarthy
Giorgio Metta

We investigated the use of optical flow to predict the time and location of impact of an incoming object. By examining local patterns of optical flow, we make predictions on an object's trajectory with respect to a stationary observer, and its time-to-contact with the observer's (assumed planar) body. Such a cue may serve as inputs to motor responses for reactive threat avoidance, or object interception (such as catching a ball). The presented approach is based on the observed behaviour of neurons in the F4 region of the pre-motor cortex of primates and it is part of a larger project which aims at modelling multi-sensory neurons and their contribution to reaching behaviour on the iCub humanoid platform. We present preliminary experimental results of a computation model of F4's visual responses using real image sequences acquired from the robot, and demonstrate their application in a real-time threat detection/prediction system.

ICRA Conference 2011 Conference Paper

Incremental learning of robot dynamics using random features

Arjan Gijsberts
Giorgio Metta

Analytical models for robot dynamics often perform suboptimally in practice, due to various non-linearities and the difficulty of accurately estimating the dynamic parameters. Machine learning techniques are less sensitive to these problems and therefore an interesting alternative for modeling robot dynamics. We propose a learning method that combines a least squares algorithm with a non-linear feature mapping and an efficient update rule. Using data from five different robots, we show that the method can accurately model manipulator dynamics, either when trained in batch or incrementally. Furthermore, the update time and memory usage of the method are bounded, therefore allowing use in real-time control loops.

IROS Conference 2011 Conference Paper

Online multiple instance learning applied to hand detection in a humanoid robot

Carlo Ciliberto
Fabrizio Smeraldi
Lorenzo Natale
Giorgio Metta

We propose an algorithm for the visual detection and localisation of the hand of a humanoid robot. This algorithm imposes low requirements on the type of supervision required to achieve good performance. In particular the system performs feature selection and adaptation using images that are only labelled as containing the hand or not, without any explicit segmentation. Our algorithm is an online variant of Multiple Instance Learning based on boosting. Experiments in real-world conditions on the iCub humanoid robot confirm that the algorithm can learn the visual appearance of the hand, reaching an accuracy comparable with its off-line version. This remains true when supervision is generated by the robot itself in a completely autonomous fashion. Algorithms with weak supervision requirements like the one we describe are useful for autonomous robots that learn and adapt online to a changing environment. The algorithm is not hand-specific and could be easily applied to wide range of problems involving visual recognition of generic objects.

IROS Conference 2011 Conference Paper

Reexamining Lucas-Kanade method for real-time independent motion detection: Application to the iCub humanoid robot

Carlo Ciliberto
Ugo Pattacini
Lorenzo Natale
Francesco Nori
Giorgio Metta

Visual motion is a simple yet powerful cue widely used by biological systems to improve their perception and adaptation to the environment. Examples of tasks that greatly benefit from the ability to detect movement are object segmentation, 3D scene reconstruction and control of attention. In computer vision several algorithms for computing visual motion and optic flow exist. However their application in robotics is not straightforward as in these platforms visual motion is often dominated by (self) motion produced by the movement of the robot (egomotion) making it difficult to disambiguate between motion induced by the scene dynamics or by the own actions of the robot. Independent motion detection is an active field in computer vision and robotics, however approaches in this area typically require that some models of both the environment and the robot visual system are available and are hardly suitable for real-time control. In this paper we describe the motionCUT, a derivation of the Lucas-Kanade optical flow algorithm that allows detecting moving objects, irrespectively of the egomotion produced by the robot. Our method is purely visual and does not require information other than the images coming from the cameras. As such it can be easily adapted to any robotic platform. The system was tested on a stereo tracking task on the iCub humanoid robot, demonstrating that the algorithm performs well and can easily execute in real-time.

IROS Conference 2011 Conference Paper

Skin spatial calibration using force/torque measurements

Andrea Del Prete
Simone Denei
Lorenzo Natale
Fulvio Mastrogiovanni
Francesco Nori
Giorgio Cannata
Giorgio Metta

This paper deals with the problem of estimating the position of tactile elements (i. e. taxels) that are mounted on a robot body part. This problem arises with the adoption of tactile systems with a large number of sensors, and it is particularly critical in those cases in which the system is made of flexible material that is deployed on a curved surface. In this scenario the location of each taxel is partially unknown and difficult to determine manually. Placing the device is in fact an inaccurate procedure that is affected by displacements in both position and orientation. Our approach is based on the idea that it is possible to automatically infer the position of the taxels by measuring the interaction forces exchanged between the sensorized part and the environment. The location of the contact is estimated through force/torque (F/T) measures gathered by a sensor mounted on the kinematic chain of the robot. Our method requires few hypotheses and can be effectively implemented on a real platform, as demonstrated by the experiments with the iCub humanoid robot.

IROS Conference 2011 Conference Paper

Towards a platform-independent cooperative human-robot interaction system: II. Perception, execution and imitation of goal directed actions

Stéphane Lallée
Ugo Pattacini
Jean-David Boucher
Séverin Lemaignan
Alexander Lenz
Chris Melhuish
Lorenzo Natale
Sergey Skachek

If robots are to cooperate with humans in an increasingly human-like manner, then significant progress must be made in their abilities to observe and learn to perform novel goal directed actions in a flexible and adaptive manner. The current research addresses this challenge. In CHRIS. I [1], we developed a platform-independent perceptual system that learns from observation to recognize human actions in a way which abstracted from the specifics of the robotic platform, learning actions including “put X on Y” and “take X”. In the current research, we extend this system from action perception to execution, consistent with current developmental research in human understanding of goal directed action and teleological reasoning. We demonstrate the platform independence with experiments on three different robots. In Experiments 1 and 2 we complete our previous study of perception of actions “put” and “take” demonstrating how the system learns to execute these same actions, along with new related actions “cover” and “uncover” based on the composition of action primitives “grasp X” and “release X at Y”. Significantly, these compositional action execution specifications learned on one iCub robot are then executed on another, based on the abstraction layer of motor primitives. Experiment 3 further validates the platform-independence of the system, as a new action that is learned on the iCub in Lyon is then executed on the Jido robot in Toulouse. In Experiment 4 we extended the definition of action perception to include the notion of agency, again inspired by developmental studies of agency attribution, exploiting the Kinect motion capture system for tracking human motion. Finally in Experiment 5 we demonstrate how the combined representation of action in terms of perception and execution provides the basis for imitation. This provides the basis for an open ended cooperation capability where new actions can be learned and integrated into shared plans for cooperation. Part of the novelty of this research is the robots' use of spoken language understanding and visual perception to generate action representations in a platform independent manner based on physical state changes. This provides a flexible capability for goal-directed action imitation.

IROS Conference 2010 Conference Paper

A tactile sensor for the fingertips of the humanoid robot iCub

Alexander Schmitz
Marco Maggiali
Lorenzo Natale
Bruno Bonino
Giorgio Metta

In order to successfully perform object manipulation, humanoid robots must be equipped with tactile sensors. However, the limited space that is available in robotic fingers imposes severe design constraints. In [1] we presented a small prototype fingertip which incorporates a capacitive pressure system. This paper shows an improved version, which has been integrated on the hand of the humanoid robot iCub. The fingertip is 14. 5 mm long and 13 mm wide. The capacitive pressure sensor system has 12 sensitive zones and includes the electronics to send the 12 measurements over a serial bus with only 4 wires. Each synthetic fingertip is shaped approximately like a human fingertip. Furthermore, an integral part of the capacitive sensor is soft silicone foam, and therefore the fingertip is compliant. We describe the structure of the fingertip, their integration on the humanoid robot iCub and present test results to show the characteristics of the sensor.

IROS Conference 2010 Conference Paper

An experimental evaluation of a novel minimum-jerk cartesian controller for humanoid robots

Ugo Pattacini
Francesco Nori
Lorenzo Natale
Giorgio Metta
Giulio Sandini

In this paper we describe the design of a Cartesian Controller for a generic robot manipulator. We address some of the challenges that are typically encountered in the field of humanoid robotics. The solution we propose deals with a large number of degrees of freedom, produce smooth, human-like motion and is able to compute the trajectory on-line. In this paper we support the idea that to produce significant advancements in the field of robotics it is important to compare different approaches not only at the theoretical level but also at the implementation level. For this reason we test our software on the iCub platform and compare its performance against other available solutions.

IROS Conference 2010 Conference Paper

Approximate optimal control for reaching and trajectory planning in a humanoid robot

Serena Ivaldi
Matteo Fumagalli 0001
Francesco Nori
Marco Baglietto
Giorgio Metta
Giulio Sandini

Online optimal planning of robotic arm movement is addressed. Optimality is inspired by computational models, where a “cost function” is used to describe limb motions according to different criteria. A method is proposed to implement optimal planning in Cartesian space, minimizing some cost function, by means of numerical approximation to a generalized nonlinear model predictive control problem. The Extended RItz Method is applied as a functional approximation technique. Differently from other approaches, the proposed technique can be applied on platforms with strict control temporal constraints and limited processing capability, since the computational burden is completely concentrated in an off-line phase. The trajectory generation on-line is therefore computationally efficient. Task to joint space conversion is implemented on-line by a closed loop inverse kinematics algorithm, taking into account the robot's physical limits. Experimental results, where a 4DOF arm moves according to a particular nonlinear cost, show the effectiveness of the proposed approach, and suggest interesting future developments.

IROS Conference 2010 Conference Paper

Exploiting proximal F/T measurements for the iCub active compliance

Matteo Fumagalli 0001
Marco Randazzo
Francesco Nori
Lorenzo Natale
Giorgio Metta
Giulio Sandini

During the last decades, interaction (with humans and with the environment) has become an increasingly interesting topic of research within the field of robotics. At the basis of interaction, a fundamental role is played by the ability to actively regulate the interaction forces. In this paper we propose a technique for controlling the interaction forces exploiting a proximal six axes force/torque sensor. The major assumption is the knowledge of the point where external forces are applied. The proposed approach is tested and validated on the four limbs of the iCub, a humanoid robot designed for research in embodied cognition. Remarkably, the proposed approach can be used to implement active compliance in other non passively back-drivable manipulators by simply inserting one or more force/torque sensor anywhere along the kinematic chain.

ICRA Conference 2010 Conference Paper

Machine-learning based control of a human-like tendon-driven neck

Lorenzo Jamone
Matteo Fumagalli 0001
Giorgio Metta
Lorenzo Natale
Francesco Nori
Giulio Sandini

This paper describes the control of a human-like robotic neck actuated with tendons. The controller regulates the length of the tendons to achieve a desired orientation of the neck and at the same time it maintains the tension of the tendons within certain limits. The solution we propose does not use any model of the system, but it relies on online learning of the different Jacobian mappings required by the controller. Learning, data acquisition and control are simultaneous; thus learning is completely autonomous, and purely online. We show that after enough iterations the controller produces straight trajectories in the task space and is able to maintain the tension of the tendons within safe limits.

IROS Conference 2010 Conference Paper

Object recognition using visuo-affordance maps

Arjan Gijsberts
Tatiana Tommasi
Giorgio Metta
Barbara Caputo

One of the major challenges in developing autonomous systems is to make them able to recognize and categorize objects robustly. However, the appearance-based algorithms that are widely employed for robot perception do not explore the functionality of objects, described in terms of their affordances. These affordances (e. g. , manipulation, grasping) are discriminative for object categories and are important cues for reliable robot performance in everyday environments. In this paper, we propose a strategy for object recognition that integrates both visual appearance and grasp affordance features. Following previous work, we hypothesize that additional grasp information improves object recognition, even if we reconstruct the grasp modality from visual features using a mapping function. We considered two different representations for the grasp modality: (1) motor information of the hand posture while grasping and (2) a more general grasp affordance descriptor. Using a multi-modal classifier we show that having real grasp information significantly boost object recognition. This improvement is preserved, although to a lesser extent, if the grasp modality is reconstructed using the mapping function.

IROS Conference 2010 Conference Paper

Own body perception based on visuomotor correlation

Ryo Saegusa
Giorgio Metta
Giulio Sandini

This work proposes a plausible approach for a humanoid robot to define its own body parts based on the correlation of two different sensory signals: vision and proprioception. The high correlation between the motions in vision and proprioception informs the robot that the visually attractive object is related to the motor function of its own body. When the robot finds the highly motor-correlated object during head-arm movements, the visuomotor cues such as the body posture and visual features are stored in a visuomotor memory. Then, developmentally, the robot defines the motor-correlated objects as the own-body parts without prior knowledge on the body appearances and kinematics. It is also adaptable to extended body parts such as a grasped tool. The body movements are generated by stochastic motor babbling. The visuomotor memory biases the babbling to keep the own-body parts in sight. This memory-based bias towards the own-body parts helps the robot explore the large head-arm joint space. The acquired visuomotor memory is also used to anticipate the own-body image from the motor commands in advance of the body movement. The proposed approach was evaluated with two humanoid platforms; iCub and James.

ICRA Conference 2010 Conference Paper

Safe and effective learning: A case study

Giorgio Metta
Lorenzo Natale
Shashank Pathak
Luca Pulina
Armando Tacchella

In this paper we consider the problem of ensuring that a multi-agent robot control system is both safe and effective in the presence of learning components. Safety, i. e. , proving that a potentially dangerous configuration is never reached in the control system, usually competes with effectiveness, i. e. , ensuring that tasks are performed at an acceptable level of quality. In particular, we focus on a robot playing the air hockey game against a human opponent, where the robot has to learn how to minimize opponent's goals (defense play). This setup is paradigmatic since the robot must see, decide and move fastly, but, at the same time, it must learn and guarantee that the control system is safe throughout the process. We attack this problem using automata-theoretic formalisms and associated verification tools, showing experimentally that our approach can yield safety without heavily compromising effectiveness.

IROS Conference 2009 Conference Paper

Active learning for multiple sensorimotor coordination based on state confidence

Ryo Saegusa
Giorgio Metta
Giulio Sandini

For a complex autonomous robotic system such as a humanoid robot, motor-babbling-based sensorimotor learning is considered an effective method to develop an internal model of the self-body and the environment autonomously. However, learning process requires much time for exploration and computation. In this paper, we propose a method of sensorimotor learning which explores the learning domain actively. Our approach discovers that the embodied learning system can design its own learning process actively, which is different from the conventional passive data-access machine learning. The proposed model is characterized by a function we call the “confidence”, and is a measure of the reliability of state control. The confidence for the state can be a good measure to bias the exploration strategy of data sampling, and to direct its attention to areas of learning interest. We consider the confidence function to be a first step toward an active behavior design for autonomous environment adaptation. The approach was experimentally validated in typical sensorimotor coordination such as arm reaching and object fixation, using the humanoid robot James and the iCub simulator.

IROS Conference 2009 Conference Paper

Composing and coordinating body models of arbitrary connectivity and redundancy: A biomimmetic field computing approach

Vishwanathan Mohan
Jacopo Zenzeri
Pietro G. Morasso
Giorgio Metta

The subjective ease with which we move gracefully in constraint filled uncertain environments often masks the enormously complex integrative apparatus needed to spell synergy among the thousands of sensors, joints, musculo-skeletal units and neuronal populations that contribute to any act's planning and execution. In this paper, we apply the computational framework of passive motion paradigm for task specific composition and coordination of the movements of a limb, network of limbs (e. g. left arm-waist-right arm) or networks of external objects coupled to the body of the 53 degrees of freedom humanoid robot `iCub'. The basic PMP model is further extended by formulation of a pair of branching nodes that allow compositionality and transfer of force fields from one relaxation network to another. The generality of the proposed approach is further illustrated using simulations of whole body reaching (WBR) tasks from a quiet standing posture that recruits virtually all the joints of the upper limbs, lower limbs, and trunk, binding together a large number of degrees of freedom into a functional unit that combines a focal task (reaching a target with the hand) and a postural task (keeping the projection of the center of mass within the bipedal support area). Preliminary comparisons of the solutions generated by the computational model with the movements of human subjects performing similar WBR tasks are presented.

IROS Conference 2007 Conference Paper

Autonomous learning of 3D reaching in a humanoid robot

Francesco Nori
Lorenzo Natale
Giulio Sandini
Giorgio Metta

In this paper, we describe the implementation of a precise reaching controller on an upper-torso humanoid robot. The solution we propose does not rely on prior models of the kinematic structure of either the arm or the head. A learning strategy enables the robot to acquire the required sensory-motor transformations. After learning the robot is able to precisely reach for a visually identified object in the 3-dimensional space. In this technique we use the fixation point (represented in the head joints motor space) as a reference frame to code the position of the object and to represent the eye-to-hand Jacobian matrix. This strategy successfully deals with the kinematic redundancy of the structure and constraints the dimensionality of the problem.

ICRA Conference 2006 Conference Paper

Design of the Robot-cub (iCub) Head

Ricardo Beira
Manuel Lopes 0001
Miguel Praça
José Santos-Victor
Alexandre Bernardino
Giorgio Metta
Francesco Becchi
Roque J. Saltarén

This paper describes the design of a robot head, developed in the framework of the RobotCub project. This project goals consists on the design and construction of a humanoid robotic platform, the iCub, for studying human cognition. The final platform would be approximately 90 cm tall, with 23 kg and with a total number of 53 degrees of freedom. For its size, the iCub is the most complete humanoid robot currently being designed, in terms of kinematic complexity. The eyes can also move, as opposed to similarly sized humanoid platforms. Specifications are made based on biological anatomical and behavioral data, as well as tasks constraints. Different concepts for the neck design (flexible, parallel and serial solutions) are analyzed and compared with respect to the specifications. The eye structure and the proprioceptive sensors are presented, together with some discussion of preliminary work on the face design

ICRA Conference 2003 Conference Paper

Learning about objects through action -initial steps towards artificial cognition

Paul M. Fitzpatrick
Giorgio Metta
Lorenzo Natale
Ajit Rao
Giulio Sandini

Within the field of Neuro Robotics we are driven primarily by the desire to understand how humans and animals live and grow and solve every day's problems. To this aim we adopted a "learn by doing" approach by building artificial systems, e. g. robots that not only look like human beings but also represent a model of some brain process. They should, ideally, behave and interact like human beings (being situated). The main emphasis in robotics has been on systems that act as a reaction to an external stimulus (e. g. tracking, reaching), rather than as a result of an internal drive to explore or "understand" the environment. We think it is now appropriate to try to move from acting, in the sense explained above, to "understanding". As a starting point we addressed the problem of learning about the effects and consequences of self-generated actions. How does the robot learn how to pull an object toward itself or to push it away? How does the robot learn that spherical objects roll while a cube only slides if pushed? Interacting with objects is important because it implicitly explores object representation, event understanding, and can provide definition of object-hood that could not be grasped with a mere passive observation of the world. Further, learning to understand what one's own body can do is an essential step toward learning by imitation. In this view two actions are similar not only if their kinematics and dynamics are similar but rather if the effects on the external world are the same. Along this line of research we discuss some recent experiments performed at the AI-Lab at MIT and at the LIRA-Lab at the University of Genova on COG and Babybot respectively. We show how the humanoid robots can learn how to poke and prod objects to obtain a consistently repeatable effect (e. g. sliding in a given direction), to help visual segmentation, and to interpret a poking action performed by a human manipulator.

IROS Conference 2002 Conference Paper

Towards manipulation-driven vision

Paul M. Fitzpatrick
Giorgio Metta

For the purposes of manipulation, we would like to know what parts of the environment are physically coherent ensembles, that is, which parts will move together, and which are more or less independent. It takes a great deal of experience before this judgement can be made from purely visual information. This paper develops active strategies for acquiring that experience through experimental manipulation, using tight correlations between arm motion and optic flow to detect both the arm itself and the boundaries of objects with which it comes into contact.