Author name cluster

Michael N. Mistry

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

31 papers

1 author row

ICRA Conference 2024 Conference Paper

Robust and Dexterous Dual-arm Tele-Cooperation using Adaptable Impedance Control

Keyhan Kouhkiloui Babarahmati
Mohammadreza Kasaei 0001
Carlo Tiseo
Michael N. Mistry
Sethu Vijayakumar

In recent years, the need for robots to transition from isolated industrial tasks to shared environments, including human-robot collaboration and teleoperation, has become increasingly evident. Building on the foundation of Fractal Impedance Control (FIC) introduced in our previous work, this paper presents a novel extension to dualarm tele-cooperation, leveraging the non-linear stiffness and passivity of FIC to adapt to diverse cooperative scenarios. Unlike traditional impedance controllers, our approach ensures stability without relying on energy tanks, as demonstrated in our prior research. In this paper, we further extend the FIC framework to bimanual operations, allowing for stable and smooth switching between different dynamic tasks without gain tuning. We also introduce a telemanipulation architecture that offers higher transparency and dexterity, addressing the challenges of signal latency and low-bandwidth communication. Through extensive experiments, we validate the robustness of our method and the results confirm the advantages of the FIC approach over traditional impedance controllers, showcasing its potential for applications in planetary exploration and other scenarios requiring dexterous telemanipulation. This paper’s contributions include the seamless integration of FIC into multi-arm systems, the ability to perform robust interactions in highly variable environments, and the provision of a comprehensive comparison with competing approaches, thereby significantly enhancing the robustness and adaptability of robotic systems.

IROS Conference 2022 Conference Paper

Efficient Learning of Inverse Dynamics Models for Adaptive Computed Torque Control

David Jorge
Gabriella Pizzuto
Michael N. Mistry

Modelling robot dynamics accurately is essential for control, motion optimisation and safe human-robot collaboration. Given the complexity of modern robotic systems, dynamics modelling remains non-trivial, mostly in the presence of compliant actuators, mechanical inaccuracies, friction and sensor noise. Recent efforts have focused on utilising datadriven methods such as Gaussian processes and neural networks to overcome these challenges, as they are capable of capturing these dynamics without requiring extensive knowledge beforehand. While Gaussian processes have shown to be an effective method for learning robotic dynamics with the ability to also represent the uncertainty in the learned model through its variance, they come at a cost of cubic time complexity rather than linear, as is the case for deep neural networks. In this work, we leverage the use of deep kernel models, which combine the computational efficiency of deep learning with the nonparametric flexibility of kernel methods (Gaussian processes), with the overarching goal of realising an accurate probabilistic framework for uncertainty quantification. Through using the predicted variance, we adapt the feedback gains as more accurate models are learned, leading to low-gain control without compromising tracking accuracy. Using simulated and real data recorded from a seven degree-of-freedom robotic manipulator, we illustrate how using stochastic variational inference with deep kernel models increases compliance in the computed torque controller, and retains tracking accuracy. We empirically show how our model outperforms current state-of-the-art methods with prediction uncertainty for online inverse dynamics model learning, and solidify its adaptation and generalisation capabilities across different setups.

ICRA Conference 2022 Conference Paper

Optimal Control via Combined Inference and Numerical Optimization

Daniel Layeghi
Steve Tonneau
Michael N. Mistry

Derivative based optimization methods are efficient at solving optimal control problems near local optima. However, their ability to converge halts when derivative information vanishes. The inference approach to optimal control does not have strict requirements on the objective landscape. However, sampling, the primary tool for solving such problems, tends to be much slower in computation time. We propose a new method that combines second order methods with inference. We utilise the Kullback Leibler (KL) control framework to formulate an inference problem that computes the optimal controls from an adaptive distribution approximating the solution of the second order method. Our method allows for combining simple convex and non convex cost functions. This simplifies the process of cost function design and leverages the strengths of both inference and second order optimization. We compare our method to Model Predictive Path Integral (MPPI) and iterative Linear Quadratic Gaussian controller (iLQG), outperforming both in sample efficiency and quality on manipulation and obstacle avoidance tasks.

ICRA Conference 2022 Conference Paper

Robust Impedance Control for Dexterous Interaction Using Fractal Impedance Controller with IK-Optimisation

Carlo Tiseo
Quentin Rouxel
Zhibin Li 0001
Michael N. Mistry

Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human movements. However, they are often not robust and their generalisation is limited. This work proposed a hierarchical control architecture for robot manipulators and provided capabilities of reproducing human-like motions during unknown interaction dynamics. Our results show that the reproduced end-effector trajectories can preserve the main characteristics of the initial human motion recorded via a motion capture system, and are robust against external perturbations. The data indicate that some detailed movements are hard to reproduce due to the physical limits of the hardware that cannot reach the same velocity recorded in human movements. Nevertheless, these technical problems can be addressed by using better hardware and our proposed algorithms can still be applied to produce imitated motions.

ICRA Conference 2021 Conference Paper

A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots

Carlo Tiseo
Vladimir Ivan
Wolfgang Merkt
Ioannis Havoutis
Michael N. Mistry
Sethu Vijayakumar

Path planning and collision avoidance are challenging in complex and highly variable environments due to the limited horizon of events. In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality. We propose a planning algorithm based on a globally stable passive controller that can plan smooth trajectories using limited computational resources in challenging environmental conditions. The architecture combines the recently proposed fractal impedance controller with elastic bands and regions of finite time invariance. As the method is based on an impedance controller, it can also be used directly as a force/torque controller. We validated our method in simulation to analyse the ability of interactive navigation in challenging concave domains via the issuing of via-points, and its robustness to low bandwidth feedback. A swarm simulation using 11 agents validated the scalability of the proposed method. We have performed hardware experiments on a holonomic wheeled platform validating smoothness and robustness of interaction with dynamic agents (i. e. , humans and robots). The computational complexity of the proposed local planner enables deployment with low-power micro-controllers lowering the energy consumption compared to other methods that rely upon numeric optimisation.

ICRA Conference 2021 Conference Paper

Online Dynamic Trajectory Optimization and Control for a Quadruped Robot

Oguzhan Cebe
Carlo Tiseo
Guiyang Xin
Hsiu-Chin Lin
Joshua Smith 0002
Michael N. Mistry

Legged robot locomotion requires the planning of stable reference trajectories, especially while traversing uneven terrain. The proposed trajectory optimization framework is capable of generating dynamically stable base and footstep trajectories for multiple steps. The locomotion task can be defined with contact locations, base motion or both, making the algorithm suitable for multiple scenarios (e. g. , presence of moving obstacles). The planner uses a simplified momentum-based task space model for the robot dynamics, allowing computation times that are fast enough for online replanning. This fast planning capability also enables the quadruped to accommodate for drift and environmental changes. The algorithm is tested on simulation and a real robot across multiple scenarios, which includes uneven terrain, stairs and moving obstacles. The results show that the planner is capable of generating stable trajectories in the real robot even when a box of 15 cm height is placed in front of its path at the last moment.

ICRA Conference 2021 Conference Paper

Robust High-Transparency Haptic Exploration for Dexterous Telemanipulation

Keyhan Kouhkiloui Babarahmati
Carlo Tiseo
Quentin Rouxel
Zhibin Li 0001
Michael N. Mistry

Robotic teleoperation provides human-in-the-loop capabilities of complex manipulation tasks in dangerous or remote environments, such as for planetary exploration or nuclear decommissioning. This work proposes a novel telemanipulation architecture using a passive Fractal Impedance Controller (FIC), which does not depend upon an active viscous component for guaranteeing stability. Compared to a traditional impedance controller in ideal conditions (no delays and maximum communication bandwidth), our proposed method yields higher transparency in interaction and demonstrates superior dexterity and capability in our telemanipulation test scenarios. We also validate its performance with extreme delays up to 1 s and communication bandwidths as low as 10 Hz. All results validate a consistent stability when using the proposed controller in challenging conditions, regardless of operator expertise.

IROS Conference 2020 Conference Paper

Adversarial Generation of Informative Trajectories for Dynamics System Identification

Marija Jegorova
Joshua Smith 0002
Michael N. Mistry
Timothy M. Hospedales

Dnamic System Identification approaches usually heavily rely on evolutionary and gradient-based optimisation techniques to produce optimal excitation trajectories for determining the physical parameters of robot platforms. Current optimisation techniques tend to generate single trajectories. This is expensive, and intractable for longer trajectories, thus limiting their efficacy for system identification. We propose to tackle this issue by using multiple shorter cyclic trajectories, which can be generated in parallel, and subsequently combined together to achieve the same effect as a longer trajectory. Crucially, we show how to scale this approach even further by increasing the generation speed and quality of the dataset through the use of generative adversarial network (GAN) based architectures to produce large databases of valid and diverse excitation trajectories. To the best of our knowledge, this is the first robotics work to explore system identification with multiple cyclic trajectories and to develop GAN-based techniques for scaleably producing excitation trajectories that are diverse in both control parameter and inertial parameter spaces. We show that our approach dramatically accelerates trajectory optimisation, while simultaneously providing more accurate system identification than the conventional approach.

ICRA Conference 2020 Conference Paper

Bounded haptic teleoperation of a quadruped robot's foot posture for sensing and manipulation

Guiyang Xin
Joshua Smith 0002
David Rytz
Wouter Jan Wolfslag
Hsiu-Chin Lin
Michael N. Mistry

This paper presents a control framework to teleoperate a quadruped robot's foot for operator-guided haptic exploration of the environment. Since one leg of a quadruped robot typically only has 3 actuated degrees of freedom (DoFs), the torso is employed to assist foot posture control via a hierarchical whole-body controller. The foot and torso postures are controlled by two analytical Cartesian impedance controllers cascaded by a null space projector. The contact forces acting on supporting feet are optimized by quadratic programming (QP). The foot's Cartesian impedance controller may also estimate contact forces from trajectory tracking errors, and relay the force-feedback to the operator. A 7D haptic joystick, Sigma. 7, transmits motion commands to the quadruped robot ANYmal, and renders the force feedback. Furthermore, the joystick's motion is bounded by mapping the foot's feasible force polytope constrained by the friction cones and torque limits in order to prevent the operator from driving the robot to slipping or falling over. Experimental results demonstrate the efficiency of the proposed framework.

ICRA Conference 2020 Conference Paper

Contact Surface Estimation via Haptic Perception

Hsiu-Chin Lin
Michael N. Mistry

Legged systems need to optimize contact force in order to maintain contacts. For this, the controller needs to have the knowledge of the surface geometry and how slippery the terrain is. We can use a vision system to realize the terrain, but the accuracy of the vision system degrades in harsh weather, and it cannot visualize the terrain if it is covered with water or grass. Also, the degree of friction cannot be directly visualized. In this paper, we propose an online method to estimate the surface information via haptic exploration. We also introduce a probabilistic criterion to measure the quality of the estimation. The method is validated on both simulation and a real robot platform.

IROS Conference 2019 Conference Paper

Online Optimal Impedance Planning for Legged Robots

Franco Angelini
Guiyang Xin
Wouter Jan Wolfslag
Carlo Tiseo
Michael N. Mistry
Manolo Garabini
Antonio Bicchi
Sethu Vijayakumar

Real world applications require robots to operate in unstructured environments. This kind of scenarios may lead to unexpected environmental contacts or undesired interactions, which may harm people or impair the robot. Adjusting the behavior of the system through impedance control techniques is an effective solution to these problems. However, selecting an adequate impedance is not a straightforward process. Normally, robot users manually tune the controller gains with trial and error methods. This approach is generally slow and requires practice. Moreover, complex tasks may require different impedance during different phases of the task. This paper introduces an optimization algorithm for online planning of the Cartesian robot impedance to adapt to changes in the task, robot configuration, expected disturbances, external environment and desired performance, without employing any direct force measurements. We provide an analytical solution leveraging the mass-spring-damper behavior that is conferred to the robot body by the Cartesian impedance controller. Stability during gains variation is also guaranteed. The effectiveness of the method is experimentally validated on the quadrupedal robot ANYmal. The variable impedance helps the robot to tackle challenging scenarios like walking on rough terrain and colliding with an obstacle.

ICRA Conference 2019 Conference Paper

Single-shot Foothold Selection and Constraint Evaluation for Quadruped Locomotion

Dominik Belter
Jakub Bednarek
Hsiu-Chin Lin
Guiyang Xin
Michael N. Mistry

In this paper, we propose a method for selecting the optimal footholds for legged systems. The goal of the proposed method is to find the best foothold for the swing leg on a local elevation map. First, we evaluate the geometrical characteristics of each cell on the elevation map, checks kinematic constraints and collisions. Then, we apply the Convolutional Neural Network to learn the relationship between the local elevation map and the quality of potential footholds. During execution time, the controller obtains the qualitative measurement of each potential foothold from the neural model. This method evaluates hundreds of potential footholds and checks multiple constraints in a single step which takes 10 ms on a standard computer without GPU. The experiments were carried out on a quadruped robot walking over rough terrain in both simulation and real robotic platforms.

ICRA Conference 2018 Conference Paper

A Model-Based Hierarchical Controller for Legged Systems Subject to External Disturbances

Guiyang Xin
Hsiu-Chin Lin
Joshua Smith 0002
Oguzhan Cebe
Michael N. Mistry

Legged robots have many potential applications in real-world scenarios where the tasks are too dangerous for humans, and compliance is needed to protect the system against external disturbances and impacts. In this paper, we propose a model-based controller for hierarchical tasks of legged systems subject to external disturbance. The control framework is based on projected inverse dynamics controller, such that the control law is decomposed into two orthogonal subspaces, i. e. , the constrained and the unconstrained subspaces. The unconstrained component controls multiple desired tasks with impedance responses. The constrained space controller maintains the contact subject to unknown external disturbances, without the use of any force/torque sensing at the contact points. By explicitly modelling the external force, our controller is robust to external disturbances and errors arising from incorrect dynamic model information. The main contributions of this paper include (1) incorporating an impedance controller to control external disturbances and allow impedance shaping to adjust the behaviour of the motion under external disturbances, (2) optimising contact forces within the constrained subspace that also takes into account the external disturbances without using force/torque sensors at the contact locations. The techniques are evaluated on the ANYmal quadruped platform under a variety of scenarios.

ICRA Conference 2018 Conference Paper

A Projected Inverse Dynamics Approach for Multi-Arm Cartesian Impedance Control

Hsiu-Chin Lin
Joshua Smith 0002
Keyhan Kouhkiloui Babarahmati
Niels Dehio
Michael N. Mistry

We propose a model-based control framework for multi-arm manipulation of a rigid object subject to external disturbances. The control framework, based on projected inverse dynamics, decomposes the control law into constrained and unconstrained subspaces. Unconstrained components accomplish the motion task with a desired 6-DOF Cartesian impedance behaviour against external disturbances. Meanwhile, the constrained component enforces contact and friction constraints by optimising for contact forces within the constrained subspace. External disturbances are explicitly compensated for without using force/torque sensors at the contact points. The approach is evaluated on a dual-arm platform manipulating a rigid object while coping with unknown object dynamics and human interaction.

ICRA Conference 2018 Conference Paper

Modeling and Control of Multi-Arm and Multi-Leg Robots: Compensating for Object Dynamics During Grasping

Niels Dehio
Joshua Smith 0002
Dennis Leroy Wigand
Guiyang Xin
Hsiu-Chin Lin
Jochen J. Steil
Michael N. Mistry

We consider a virtual manipulator in grasping scenarios which allows us to capture the effect of the object dynamics. This modeling approach turns a multi-arm robot into an underactuated system. We observe that controlling floating-base multi-leg robots is fundamentally similar. The Projected Inverse Dynamics Control approach is employed for decoupling contact consistent motion generation and controlling contact wrenches. The proposed framework for underactuated robots has been evaluated on an enormous robot hand composed of four KUKA LWR IV+ representing fingers cooperatively manipulating a 9kg box with total 28 actuated DOF and six virtual DOF representing the object as additional free-floating robot link. Finally, we validate the same approach on ANYmal, a floating-base quadruped with 12 actuated DOF. Experiments are performed both in simulation and real world.

ICRA Conference 2017 Conference Paper

Dynamic manipulability of the center of mass: A tool to study, analyse and measure physical ability of robots

Morteza Azad
Jan Babic
Michael N. Mistry

This paper introduces dynamic manipulability of the center of mass (CoM) as a metric to measure robots' physical abilities to accelerate their CoMs in different directions. By decomposing the effects of velocity dependent constraints, such as unilateral contacts and friction cones, CoM dynamic manipulability is defined as a velocity independent metric which depends only on robot's configuration and inertial parameters. Thus, this metric is independent of any choice of controller and expresses only physical abilities of robots. This important property makes the proposed metric a proper tool to study, analyse and design current and future robots. The outcome of the CoM dynamic manipulability analysis in this paper is an ellipsoid in the CoM acceleration space which graphically shows accessible points due to the unit weighted norm of joint torques. Physical meanings and concepts of two reasonable choices for the weighting matrix, which is used in the weighted norm of joint torques, are discussed and illustrative examples are presented. Since the proposed metric measures physical ability to accelerate the CoM, it is claimed to be a suitable tool to study balance ability of legged robots.

IROS Conference 2016 Conference Paper

Vision-guided state estimation and control of robotic manipulators which lack proprioceptive sensors

Valerio Ortenzi
Naresh Marturi
Rustam Stolkin
Jeffrey A. Kuo
Michael N. Mistry

This paper presents a vision-based approach for estimating the configuration of, and providing control signals for, an under-sensored robot manipulator using a single monocular camera. Some remote manipulators, used for decommissioning tasks in the nuclear industry, lack proprioceptive sensors because electronics are vulnerable to radiation. Additionally, even if proprioceptive joint sensors could be retrofitted, such heavy-duty manipulators are often deployed on mobile vehicle platforms, which are significantly and erratically perturbed when powerful hydraulic drilling or cutting tools are deployed at the end-effector. In these scenarios, it would be beneficial to use external sensory information, e. g. vision, for estimating the robot configuration with respect to the scene or task. Conventional visual servoing methods typically rely on joint encoder values for controlling the robot. In contrast, our framework assumes that no joint encoders are available, and estimates the robot configuration by visually tracking several parts of the robot, and then enforcing equality between a set of transformation matrices which relate the frames of the camera, world and tracked robot parts. To accomplish this, we propose two alternative methods based on optimisation. We evaluate the performance of our developed framework by visually tracking the pose of a conventional robot arm, where the joint encoders are used to provide ground-truth for evaluating the precision of the vision system. Additionally, we evaluate the precision with which visual feedback can be used to control the robot's end-effector to follow a desired trajectory.

ICRA Conference 2015 Conference Paper

Balance control strategy for legged robots with compliant contacts

Morteza Azad
Michael N. Mistry

This paper proposes a momentum-based balancing controller for robots which have non-rigid contacts with their environments. This controller regulates both linear momentum and angular momentum about the center of mass of the robot by controlling the contact forces. Compliant contact models are used to determine the contact forces at the contact points. Simulation results show the performance of the controller on a four-link planar robot standing on various compliant surfaces while unknown external forces in different directions are acting on the center of mass of the robot.

IROS Conference 2015 Conference Paper

Projected inverse dynamics control and optimal control for robots in contact with the environment: A comparison

Valerio Ortenzi
Rustam Stolkin
Jeffrey A. Kuo
Michael N. Mistry

This paper addresses the problem of constrained motion for a manipulator performing a task while in contact with the environment, and investigates two force control frameworks, one based on projected inverse dynamics, and one based on optimal control. Firstly, we propose a control method based on projected inverse dynamics, which directly exploits the contact constraints to minimise the instantaneous joint torques needed to perform a task. Secondly, we propose an optimal control strategy which provides a tool to minimise the joint torques over an interval of time. We show how contact constraints can be used as optimisation constraints in the definition of the problem, and how to formulate the optimal control problem directly using projected dynamics. Initially we explore a positional control problem, where the robot is required to follow a desired path, and show that both of the proposed methods can satisfy the positional task while significantly reducing the joint torques as compared to simple kinematic control and also classical inverse dynamics control. We also show that the proposed optimal control method outperforms the pure projected inverse dynamics method in terms of minimising the required joint torques. We then show how each method can be extended to follow a desired path while also exerting a desired contact force. Again, the method incorporating optimal control is shown to satisfy the task requirements with significantly smaller commanded torques than the pure projected inverse dynamics method. To confirm the analysis, and demonstrate proof of concept, we present the results of empirical experiments with a simulated 3-degree-of-freedom planar manipulator which is constrained to move while in contact with a rigid surface.

ICRA Conference 2011 Conference Paper

Inverse dynamics control of floating-base robots with external constraints: A unified view

Ludovic Righetti
Jonas Buchli
Michael N. Mistry
Stefan Schaal

Inverse dynamics controllers and operational space controllers have proved to be very efficient for compliant control of fully actuated robots such as fixed base manipulators. However legged robots such as humanoids are inherently different as they are underactuated and subject to switching external contact constraints. Recently several methods have been proposed to create inverse dynamics controllers and operational space controllers for these robots. In an attempt to compare these different approaches, we develop a general framework for inverse dynamics control and show that these methods lead to very similar controllers. We are then able to greatly simplify recent whole-body controllers based on operational space approaches using kinematic projections, bringing them closer to efficient practical implementations. We also generalize these controllers such that they can be optimal under an arbitrary quadratic cost in the commands.

ICRA Conference 2010 Conference Paper

Fast, robust quadruped locomotion over challenging terrain

Mrinal Kalakrishnan
Jonas Buchli
Peter Pastor
Michael N. Mistry
Stefan Schaal

We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization and control techniques to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal foothold choices from expert demonstration using terrain templates, (2) a body trajectory optimizer based on the Zero-Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller by testing it on the LittleDog quadruped robot, over a wide variety of rough terrain of varying difficulty levels. We demonstrate the generalization ability of this controller by presenting test results from an independent external test team on terrains that have never been shown to us.

ICRA Conference 2010 Conference Paper

Inverse dynamics control of floating base systems using orthogonal decomposition

Michael N. Mistry
Jonas Buchli
Stefan Schaal

Model-based control methods can be used to enable fast, dexterous, and compliant motion of robots without sacrificing control accuracy. However, implementing such techniques on floating base robots, e. g. , humanoids and legged systems, is non-trivial due to under-actuation, dynamically changing constraints from the environment, and potentially closed loop kinematics. In this paper, we show how to compute the analytically correct inverse dynamics torques for model-based control of sufficiently constrained floating base rigid-body systems, such as humanoid robots with one or two feet in contact with the environment. While our previous inverse dynamics approach relied on an estimation of contact forces to compute an approximate inverse dynamics solution, here we present an analytically correct solution by using an orthogonal decomposition to project the robot dynamics onto a reduced dimensional space, independent of contact forces. We demonstrate the feasibility and robustness of our approach on a simulated floating base bipedal humanoid robot and an actual robot dog locomoting over rough terrain.

IROS Conference 2009 Conference Paper

Compliant quadruped locomotion over rough terrain

Jonas Buchli
Mrinal Kalakrishnan
Michael N. Mistry
Peter Pastor
Stefan Schaal

Many critical elements for statically stable walking for legged robots have been known for a long time, including stability criteria based on support polygons, good foothold selection, recovery strategies to name a few. All these criteria have to be accounted for in the planning as well as the control phase. Most legged robots usually employ high gain position control, which means that it is crucially important that the planned reference trajectories are a good match for the actual terrain, and that tracking is accurate. Such an approach leads to conservative controllers, i. e. relatively low speed, ground speed matching, etc. Not surprisingly such controllers are not very robust - they are not suited for the real world use outside of the laboratory where the knowledge of the world is limited and error prone. Thus, to achieve robust robotic locomotion in the archetypical domain of legged systems, namely complex rough terrain, where the size of the obstacles are in the order of leg length, additional elements are required. A possible solution to improve the robustness of legged locomotion is to maximize the compliance of the controller. While compliance is trivially achieved by reduced feedback gains, for terrain requiring precise foot placement (e. g. climbing rocks, walking over pegs or cracks) compliance cannot be introduced at the cost of inferior tracking. Thus, model-based control and - in contrast to passive dynamic walkers - active balance control is required. To achieve these objectives, in this paper we add two crucial elements to legged locomotion, i. e. , floating-base inverse dynamics control and predictive force control, and we show that these elements increase robustness in face of unknown and unanticipated perturbations (e. g. obstacles). Furthermore, we introduce a novel line-based COG trajectory planner, which yields a simpler algorithm than traditional polygon based methods and creates the appropriate input to our control system. We show results from both simulation and real world of a robotic dog walking over non-perceived obstacles and rocky terrain. The results prove the effectivity of the inverse dynamics/force controller. The presented results show that we have all elements needed for robust all-terrain locomotion, which should also generalize to other legged systems, e. g. , humanoid robots.

ICRA Conference 2008 Conference Paper

CB: Exploring neuroscience with a humanoid research platform

Gordon Cheng
Sang-Ho Hyon
Ales Ude
Jun Morimoto
Joshua G. Hale
Joseph Hart
Jun Nakanishi
Darrin C. Bentivegna

In this video presentation we introduce a 50 degrees of freedom humanoid robot, CB - Computational Brain [1]. CB is a humanoid robot created for exploring the underlying processing of the human brain while dealing with the real world. We place our investigations within real world contexts, as humans do. In so doing, we focus on utilising a system that is closer to humans - in sensing, kinematics configuration and performance.

ICRA Conference 2007 Conference Paper

A Robust Quadruped Walking Gait for Traversing Rough Terrain

Dimitris Pongas
Michael N. Mistry
Stefan Schaal

Legged locomotion excels when terrains become too rough for wheeled systems or open-loop walking pattern generators to succeed, i. e. , when accurate foot placement is of primary importance in successfully reaching the task goal. In this paper we address the scenario where the rough terrain is traversed with a static walking gait, and where for every foot placement of a leg, the location of the foot placement was selected irregularly by a planning algorithm. Our goal is to adjust a smooth walking pattern generator with the selection of every foot placement such that the COG of the robot follows a stable trajectory characterized by a stability margin relative to the current support triangle. We propose a novel parameter-ization of the COG trajectory based on the current position, velocity, and acceleration of the four legs of the robot. This COG trajectory has guaranteed continuous velocity and acceleration profiles, which leads to continuous velocity and acceleration profiles of the leg movement, which is ideally suited for advanced model-based controllers. Pitch, yaw, and ground clearance of the robot are easily adjusted automatically under any terrain situation. We evaluate our gait generation technique on the Little-Dog quadruped robot when traversing complex rocky and sloped terrains.

ICRA Conference 2007 Conference Paper

Inverse Dynamics Control with Floating Base and Constraints

Jun Nakanishi
Michael N. Mistry
Stefan Schaal

In this paper, we address the issues of compliant control of a robot under contact constraints with a goal of using joint space based pattern generators as movement primitives, as often considered in the studies of legged locomotion and biological motor control. For this purpose, we explore inverse dynamics control of constrained dynamical systems. When the system is overconstrained, it is not straightforward to formulate an inverse dynamics control law since the problem becomes an ill-posed one, where infinitely many combinations of joint torques are possible to achieve the desired joint accelerations. The goal of this paper is to develop a general and computationally efficient inverse dynamics algorithm for a robot with a free floating base and constraints. We suggest an approximate way of computing inverse dynamics algorithm by treating constraint forces computed with a Lagrange multiplier method as simply external forces based on Featherstone's floating base formulation of inverse dynamics. We present how all the necessary quantities to compute our controller can be efficiently extracted from Featherstone's spatial notation of robot dynamics. We evaluate the effectiveness of the suggested approach on a simulated biped robot model

IROS Conference 2007 Conference Paper

Task space control with prioritization for balance and locomotion

Michael N. Mistry
Jun Nakanishi
Stefan Schaal

This paper addresses locomotion with active balancing, via task space control with prioritization. The center of gravity (COG) and foot of the swing leg are treated as task space control points. Floating base inverse kinematics with constraints is employed, thereby allowing for a mobile platform suitable for locomotion. Different techniques of task prioritization are discussed and we clarify differences and similarities of previous suggested work. Varying levels of prioritization for control are examined with emphasis on singularity robustness and the negative effects of constraint switching. A novel controller for task space control of balance and locomotion is developed which attempts to address singularity robustness, while minimizing discontinuities created by constraint switching. Controllers are evaluated using a quadruped robot simulator engaging in a locomotion task.

IROS Conference 2007 Conference Paper

Towards compliant humanoids-an experimental assessment of suitable task space position/orientation controllers

Jun Nakanishi
Michael N. Mistry
Jan Peters 0001
Stefan Schaal

Compliant control will be a prerequisite for humanoid robotics if these robots are supposed to work safely and robustly in human and/or dynamic environments. One view of compliant control is that a robot should control a minimal number of degrees-of-freedom (DOFs) directly, i. e. , those relevant DOFs for the task, and keep the remaining DOFs maximally compliant, usually in the null space of the task. This view naturally leads to task space control. However, surprisingly few implementations of task space control can be found in actual humanoid robots. This paper makes a first step towards assessing the usefulness of task space controllers for humanoids by investigating which choices of controllers are available and what inherent control characteristics they have - this treatment will concern position and orientation control, where the latter is based on a quaternion formulation. Empirical evaluations on an anthropomorphic Sarcos master arm illustrate the robustness of the different controllers as well as the ease of implementing and tuning them. Our extensive empirical results demonstrate that simpler task space controllers, e. g. , classical resolved motion rate control or resolved acceleration control can be quite advantageous in face of inevitable modeling errors in model- based control, and that well chosen formulations are easy to implement and quite robust, such that they are useful for humanoids.

IROS Conference 2005 Conference Paper

A unifying methodology for the control of robotic systems

Jan Peters 0001
Michael N. Mistry
Firdaus E. Udwadia
Rick Cory
Jun Nakanishi
Stefan Schaal

Recently, R. E. Udwadia (2003) suggested to derive tracking controllers for mechanical systems using a generalization of Gauss' principle of least constraint. This method allows us to reformulate control problems as a special class of optimal control. We take this line of reasoning one step further and demonstrate that well-known and also several novel nonlinear robot control laws can be derived from this generic methodology. We show experimental verifications on a Sarcos Master Arm robot for some of the derived controllers. We believe that the suggested approach offers a promising unification and simplification of nonlinear control law design for robots obeying rigid body dynamics equations, both with or without external constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.

IROS Conference 2005 Conference Paper

An exoskeleton robot for human arm movement study

Michael N. Mistry
Peyman Mohajerian
Stefan Schaal

A new experimental platform permits us to study a novel variety of issues of human motor control, particularly full 3D movements involving the major seven degrees-of-freedom (DOF) of the human arm. We incorporate a seven DOF robot exoskeleton, and minimize weight and inertia through gravity, Coriolis, and inertia compensation, such that subjects' arm movements are largely unaffected by the manipulandum. Torque perturbations can be individually applied to any or all seven joints of the human arm, thus creating novel dynamic environments, or force fields, for subjects to respond and adapt to. Our first study investigates a joint space force field where the shoulder velocity drives a disturbing force in the elbow joint. Results demonstrate that subjects learn to compensate for the force field within about 100 trials, and, from the strong presence of aftereffects when removing the field in some randomized catch trials, that an inverse dynamics, or internal model, of the force field is formed by the nervous system. Interestingly, while after learning, hand trajectories return to baseline, joint space trajectories remained changed in response to the field, indicating that, besides learning a model of the force field, the nervous system also chose to exploit the null space to minimize the effects of the force field on the realization of the endpoint trajectory plan. We discuss applications of these results in the light of current theories of robotic control, including inverse kinematics and optimal control.

IROS Conference 2005 Conference Paper

Comparative experiments on task space control with redundancy resolution

Jun Nakanishi
Rick Cory
Michael N. Mistry
Jan Peters 0001
Stefan Schaal

Understanding the principles of motor coordination with redundant degrees of freedom still remains a challenging problem, particularly for new research in highly redundant robots like humanoids. Even after more than a decade of research, task space control with redundacy resolution still remains an incompletely understood theoretical topic, and also lacks a larger body of thorough experimental investigation on complex robotic systems. This paper presents our first steps towards the development of a working redundancy resolution algorithm which is robust against modeling errors and unforeseen disturbances arising from contact forces. To gain a better understanding of the pros and cons of different approaches to redundancy resolution, we focus on a comparative empirical evaluation. First, we review several redundancy resolution schemes at the velocity, acceleration and torque levels presented in the literature in a common notational framework and also introduce some new variants of these previous approaches. Second, we present experimental comparisons of these approaches on a seven-degree-of-freedom anthropomorphic robot arm. Surprisingly, one of our simplest algorithms empirically demonstrates the best performance, despite, from a theoretical point, the algorithm does not share the same beauty as some of the other methods. Finally, we discuss practical properties of these control algorithms, particularly in light of inevitable modeling errors of the robot dynamics.