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Adrià Colomé

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13 papers

1 author row

ICRA Conference 2025 Conference Paper

BiFold: Bimanual Cloth Folding with Language Guidance

Oriol Barbany
Adrià Colomé
Carme Torras

Cloth folding is a complex task due to the inevitable self-occlusions of clothes, their complicated dynamics, and the disparate materials, geometries, and textures that garments can have. In this work, we learn folding actions conditioned on text commands. Translating high-level, abstract instructions into precise robotic actions requires sophisticated language understanding and manipulation capabilities. To do that, we leverage a pre-trained vision-language model and repurpose it to predict manipulation actions. Our model, BiFold, can take context into account and achieves state-of-the-art performance on an existing language-conditioned folding benchmark. To address the lack of annotated bimanual folding data, we introduce a novel dataset with automatically parsed actions and language-aligned instructions, enabling better learning of text-conditioned manipulation. BiFold attains the best performance on our dataset and demonstrates strong generalization to new instructions, garments, and environments.

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ICRA Conference 2024 Conference Paper

Fitting Parameters of Linear Dynamical Systems to Regularize Forcing Terms in Dynamical Movement Primitives

Freek Stulp
Adrià Colomé
Carme Torras

Due to their flexibility and ease of use, Dynamical Movement Primitives (DMPs) are widely used in robotics applications and research. DMPs combine linear dynamical systems to achieve robustness to perturbations and adaptation to moving targets with non-linear function approximators to fit a wide range of demonstrated trajectories. We propose a novel DMP formulation with a generalized logistic function as a delayed goal system. This formulation inherently has low initial jerk, and generates the bell-shaped velocity profiles that are typical of human movement. As the novel formulation is more expressive, it is able to fit a wide range of human demonstrations well, also without a non-linear forcing term. We exploit this increased expressiveness by automating the fitting of the dynamical system parameters through opti-mization. Our experimental evaluation demonstrates that this optimization regularizes the forcing term, and improves the interpolation accuracy of parametric DMPs.

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IROS Conference 2023 Conference Paper

Quadratic Dynamic Matrix Control for Fast Cloth Manipulation

Edoardo Caldarelli
Adrià Colomé
Carlos Ocampo-Martinez
Carme Torras

Robotic cloth manipulation is an increasingly relevant area of research, challenging classic control algorithms due to the deformable nature of cloth. While it is possible to apply linear model predictive control to make the robot move the cloth according to a given reference, this approach suffers from a large dimensionality of the state-space representation of the cloth models. To address this issue, in this work we study the application of an input-output model predictive control strategy, based on quadratic dynamic matrix control, to robotic cloth manipulation. To account for uncertain disturbances on the cloth's motion, we further extend the algorithm with suitable chance constraints. In extensive simulated experiments, involving disturbances and obstacle avoidance, we show that quadratic dynamic matrix control can be successfully applied in different cloth manipulation scenarios, with significant gains in optimization speed compared to standard model predictive control strategies. The experiments further demonstrate that the closed-loop model used by quadratic dynamic matrix control can be beneficial to the tracking accuracy, leading to improvements over the standard predictive control strategy. Moreover, a preliminary experiment on a real robot shows that quadratic dynamic matrix control can indeed be employed in real settings.

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IROS Conference 2022 Conference Paper

Ordinal Inverse Reinforcement Learning Applied to Robot Learning with Small Data

Adrià Colomé
Carme Torras

Over the last decade, the ability to teach actions to robots in a user-friendly way has gained relevance, and a practical way of teaching robots a new task is to use Inverse Reinforcement Learning (IRL). In IRL, an expert teacher shows the robot a desired behaviour and an agent builds a model of the reward. The agent can also infer a policy that performs in an optimal way within the limitations of the knowledge provided to it. However, most IRL approaches assume an (almost) optimal performance of the teaching agent, which might become unpractical if the teacher is not actually an expert. In addition, most IRL focus on discrete state-action spaces that limit their applicability to certain real-world problems such as within the context of direct Policy Search (PS) reinforcement learning. Therefore, in this paper we introduce Ordinal Inverse Reinforcement Learning (OrdIRL) for continuous state variables, in which the teacher can qualitatively evaluate robot performance by selecting one among the predefined performance levels (e. g. {bad, medium, good} for three tiers of performance). Once the OrdIRL has fit an ordinal distribution to the data, we propose to use Bayesian Optimization (BO) to either gain knowledge on the inferred model (exploration) or find a policy or action that maximizes the expected reward given the prior knowledge on the reward (exploitation). In the case of large-dimensional state-action spaces, we use Dimensionality Reduction (DR) techniques and perform the BO in the latent space. Experimental results on simulation and with a robot arm show how this approach allows for learning the reward function with small data.

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IROS Conference 2020 Conference Paper

Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models

Juan Antonio Delgado-Guerrero
Adrià Colomé
Carme Torras

In the next few years, the amount and variety of context-aware robotic manipulator applications is expected to increase significantly, especially in household environments. In such spaces, thanks to programming by demonstration, non-expert people will be able to teach robots how to perform specific tasks, for which the adaptation to the environment is imperative, for the sake of effectiveness and users safety. These robot motion learning procedures allow the encoding of such tasks by means of parameterized trajectory generators, usually a Movement Primitive (MP) conditioned on contextual variables. However, naively sampled solutions from these MPs are generally suboptimal/inefficient, according to a given reward function. Hence, Policy Search (PS) algorithms leverage the information of the experienced rewards to improve the robot performance over executions, even for new context configurations. Given the complexity of the aforementioned tasks, PS methods face the challenge of exploring in high-dimensional parameter search spaces. In this work, a solution combining Bayesian Optimization, a data-efficient PS algorithm, with covariate Gaussian Process Latent Variable Models, a recent Dimensionality Reduction technique, is presented. It enables reducing dimensionality and exploiting prior demonstrations to converge in few iterations, while also being compliant with context requirements. Thus, contextual variables are considered in the latent search space, from which a surrogate model for the reward function is built. Then, samples are generated in a low-dimensional latent space, and mapped to a context-dependent trajectory. This allows us to drastically reduce the search space with the covariate GPLVM, e. g. from 105 to 2 parameters, plus a few contextual features. Experimentation in two different scenarios proves the data-efficiency and the power of dimensionality reduction of our approach.

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ICRA Conference 2020 Conference Paper

Sample-Efficient Robot Motion Learning using Gaussian Process Latent Variable Models

Juan Antonio Delgado-Guerrero
Adrià Colomé
Carme Torras

Robotic manipulators are reaching a state where we could see them in household environments in the following decade. Nevertheless, such robots need to be easy to instruct by lay people. This is why kinesthetic teaching has become very popular in recent years, in which the robot is taught a motion that is encoded as a parametric function - usually a Movement Primitive (MP)-. This approach produces trajectories that are usually suboptimal, and the robot needs to be able to improve them through trial-and-error. Such optimization is often done with Policy Search (PS) reinforcement learning, using a given reward function. PS algorithms can be classified as model-free, where neither the environment nor the reward function are modelled, or model-based, which can use a surrogate model of the reward function and/or a model for the dynamics of the task. However, MPs can become very high-dimensional in terms of parameters, which constitute the search space, so their optimization often requires too many samples. In this paper, we assume we have a robot motion task characterized with an MP of which we cannot model the dynamics. We build a surrogate model for the reward function, that maps an MP parameter latent space (obtained through a Mutual-information-weighted Gaussian Process Latent Variable Model) into a reward. While we do not model the task dynamics, using mutual information to shrink the task space makes it more consistent with the reward and so the policy improvement is faster in terms of sample efficiency.

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ICRA Conference 2020 Conference Paper

Variable Impedance Control in Cartesian Latent Space while Avoiding Obstacles in Null Space

David Parent
Adrià Colomé
Carme Torras

Human-robot interaction is one of the keys of assistive robots. Robots are expected to be compliant with people but at the same time correctly perform the tasks. In such applications, Cartesian impedance control is preferred over joint control, as the desired interaction and environmental feedback can be described more naturally, and the force to be exerted by the robot can be readily adjusted. This paper addresses the problem of controlling a robot arm in the operational space with variable stiffness so as to continuously adapt the force exerted in each phase of motion according to the precision requirements. Moreover, performing dimensionality reduction we can separate the degrees of freedom (DoF) relevant for the task from the redundant ones. The stiffness of the former can be adjusted constantly to achieve the required accuracy, while task-redundant DoF can be used to achieve other goals such as avoiding obstacles by moving in the directions where accuracy is not critical. The designed method is tested teaching the robot to give water to drink to a model of human head. Our empirical results demonstrate that the robot can learn precision requirements from demonstration. Furthermore, dimensionality reduction is proved to be useful to avoid obstacles.

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IROS Conference 2017 Conference Paper

Demonstration-free contextualized probabilistic movement primitives, further enhanced with obstacle avoidance

Adrià Colomé
Carme Torras

Movement Primitives (MPs) have been widely used over the last years for learning robot motion tasks with direct Policy Search (PS) reinforcement learning. Among them, Probabilistic Movement Primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, which benefits from the properties of probability operations. However, the generation of such ProMPs requires a set of demonstrations to capture motion variability. Additionally, using context variables to modify trajectories coded as MPs is a popular approach nowadays in order to adapt motion to environmental variables. This paper proposes a contextual representation of ProMPs that allows for an easy adaptation to changing situations through context variables, by reparametrizing motion with them. Moreover, we propose a way of initializing contextual trajectories without the need of real robot demonstrations, by setting an initial position, a final position, and a number of trajectory interest points, where the contextual variables are evaluated. The parametrizations obtained show to be accurate while relieving the user from the need of performing costly computations such as conditioning. Additionally, using this contextual representation, we propose a simple yet effective quadratic optimization-based obstacle avoidance method for ProMPs. Experiments in simulation and on a real robot show the promise of the approach.

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ICRA Conference 2015 Conference Paper

A friction-model-based framework for Reinforcement Learning of robotic tasks in non-rigid environments

Adrià Colomé
Antoni Planells
Carme Torras

Learning motion tasks in a real environment with deformable objects requires not only a Reinforcement Learning (RL) algorithm, but also a good motion characterization, a preferably compliant robot controller, and an agent giving feedback for the rewards/costs in the RL algorithm. In this paper, we unify all these parts in a simple but effective way to properly learn safety-critical robotic tasks such as wrapping a scarf around the neck (so far, of a mannequin).

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IROS Conference 2014 Conference Paper

Dimensionality reduction and motion coordination in learning trajectories with Dynamic Movement Primitives

Adrià Colomé
Carme Torras

Dynamic Movement Primitives (DMP) are nowadays widely used as movement parametrization for learning trajectories, because of their linearity in the parameters, rescaling robustness and continuity. However, when learning a movement with a robot using DMP, many parameters may need to be tuned, requiring a prohibitive number of experiments/simulations to converge to a solution with a locally or globally optimal reward.

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ICRA Conference 2014 Conference Paper

Realtime tracking and grasping of a moving object from range video

Farzad Husain
Adrià Colomé
Babette Dellen
Guillem Alenyà
Carme Torras

In this paper we present an automated system that is able to track and grasp a moving object within the workspace of a manipulator using range images acquired with a Microsoft Kinect sensor. Realtime tracking is achieved by a geometric particle filter on the affine group. Based on the tracked output, the pose of a 7-DoF WAM robotic arm is continuously updated using dynamic motor primitives until a distance measure between the tracked object and the gripper mounted on the arm is below a threshold. Then, it closes its three fingers and grasps the object. The tracker works in realtime and is robust to noise and partial occlusions. Using only the depth data makes our tracker independent of texture which is one of the key design goals in our approach. An experimental evaluation is provided along with a comparison of the proposed tracker with state-of-the-art approaches, including the OpenNI-tracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.

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ICRA Conference 2013 Conference Paper

External force estimation during compliant robot manipulation

Adrià Colomé
Diego Pardo
Guillem Alenyà
Carme Torras

This paper presents a method to estimate external forces exerted on a manipulator during motion, avoiding the use of a sensor. The method is based on task-oriented dynamics model learning and a robust disturbance state observer. The combination of both leads to an efficient torque observer that can be incorporated to any control scheme. The use of a learning-based approach avoids the need of analytical models of joints' friction or Coriolis dynamics effects.

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IROS Conference 2012 Conference Paper

Redundant inverse kinematics: Experimental comparative review and two enhancements

Adrià Colomé
Carme Torras

Motivated by the need of a robust and practical Inverse Kinematics (IK) algorithm for the WAM robot arm, we reviewed the most used closed-loop methods for redundant robots, analysing their main points of concern: convergence, numerical error, singularity handling, joint limit avoidance, and the capability of reaching secondary goals. As a result of the experimental comparison, we propose two enhancements. The first is to filter the singular values of the Jacobian matrix before calculating its pseudoinverse in order to obtain a more numerically robust result. The second is to combine a continuous task priority strategy with selective damping to generate smoother trajectories. Experimentation on the WAM robot arm shows that these two enhancements yield an IK algorithm that improves on the reviewed state-of-the-art ones, in terms of the good compromise it achieves between time step length, Jacobian conditioning, multiple task performance, and computational time, thus constituting a very solid option in practice. This proposal is general and applicable to other redundant robots.

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