Geometry-aware Manipulability Learning, Tracking and Transfer

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel manipulability transfer framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.

Adaptive sensorimotor peripersonal space representation and motor learning for a humanoid robot

Micha Hersch

This thesis presents possible computational mechanisms by which a humanoid robot can develop a coherent representation of the space within its reach (its peripersonal space), and use it to control its movements. Those mechanisms are inspired by current theories of peripersonal space representation and motor control in humans, targeting a cross-fertilization between robotics on one side, and cognitive science on the other side. This research addresses the issue of adaptivity the sensorimotor level, at the control level and at the level of simple task learning. First, this work considers the concept of body schema and suggests a computational translation of this concept, appropriate for controlling a humanoid robot. This model of the body schema is adaptive and evolves as a result of the robot sensory experience. It suggests new avenues for understanding various psychophysical and neuropsychological phenomenons of human peripersonal space representation such as adaptation to distorted vision and tool use, fake limbs experiments, body-part centered receptive fields, and multimodal neurons. Second, it is shown how the motor modality can be added to the body schema. The suggested controller is inspired by the dynamical system theory of motor control and allows the robot to simultaneously and robustly control its limbs in joint angles space and in end-effector location space. This amounts to controlling the robot in both proprioceptive and visual modalities. This multimodal control can benefit from the advantages offered by each modality and is better than traditional robotic controllers in several respects. It offers a simple and elegant solution to the singularity and joint limit avoidance problems and can be seen as a generalization of the Damped Least Square approach to robot control. The controller exhibits several properties of human reaching movements, such as quasi-straight hand paths and bell-shaped velocity profiles and non-equifinality. In a third step, the motor modalities is endowed with a statistical learning mechanism, based on Gaussian Mixture Models, that enables the humanoid to learn motor primitives from demonstrations. The robot is thus able to learn simple manipulation tasks and generalize them to various context, in a way that is robust to perturbations occurring during task execution. In addition to simulation results, the whole model has been implemented and validated on two humanoid robots, the Hoap3 and the iCub, enabling them to learn their arm and head geometries, perform reaching movements, adapt to unknown tools, and visual distortions, and learn simple manipulation tasks in a smooth, robust and adaptive way. Finally, this work hints at possible computational interpretations of the concepts of body schema, motor perception and motor primitives.

EPFL2009

Continuous extraction of task constraints in a robot programming by demonstration framework

Sylvain Calinon

Robot Programming by Demonstration (RbD), also referred to as Learning by Imitation, explores user-friendly means of teaching a robot new skills. Recent advances in RbD have identified a number of key-issues for ensuring a generic approach to the transfer of skills across various agents and contexts. This thesis focuses on the two generic questions of what-to-imitate and how-to-imitate, which are respectively concerned with the problem of extracting the essential features of a task and determining a way to reproduce these essential features in different situations. The perspective adopted in this work is that a skill can be described efficiently at a trajectory level and that the robot may infer what are the important characteristics of the skill by observing multiple demonstrations of it, assuming that the important characteristics are invariant across the demonstrations. The proposed approach is based on the use of well-established statistical methods in a RbD application, by using Hidden Markov Model (HMM) as a first approach and then moving on to the joint use of Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR). Even if these methods were applied extensively in various fields of research including human motion analysis and robotics, their use essentially focused on gesture recognition rather than on gesture reproduction. Moreover, the models were usually trained offline using large datasets. Thus, the use and combination of these machine learning techniques in a Learning by Imitation framework is challenging and has not been extensively studied yet. In this thesis, we show that these methods are well suited for incremental and active teaching scenarios where the user only provides a small number of demonstrations, either by wearing a set of motions sensors attached to his/her body, or by helping the robot refine its skill by kinesthetic teaching, that is, by embodying the robot and putting it through the motion. These techniques are then applied for enabling a Fujitsu HOAP-2 and HOAP-3 humanoid robot to extract automatically different constraints by observing several manipulation skills and to reproduce these skills in various situations through Gaussian Mixture Regression (GMR). The contributions of this thesis are threefold: (1) it contributes to RbD by proposing a generic probabilistic framework to deal with recognition, generalization, reproduction and evaluation issues, and more specifically to deal with the automatic extraction of task constraints and with the determination of a controller satisfying several constraints simultaneously to reproduce the skill in a different context; (2) it contributes to Human-Robot Interaction (HRI) by proposing active teaching methods that puts the human teacher "in the loop" of the robot's learning by using an incremental scaffolding process and by using different modalities to produce the demonstrations; (3) it finally contributes to robotics and HRI through various real-world applications of the framework showing learning and reproduction of communicative gestures and manipulation skills. The generality of the proposed framework is also demonstrated through its joint use with other learning approaches in collaborative experiments.

EPFL2007

Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework

Aude Billard, Sylvain Calinon

We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a time- independent model of a set of movements demonstrated by a human user. Two experiments are presented to validate the method, that consist of learning to hit a ball with a robotic arm, and of teaching a humanoid robot to manipulate a spoon to feed another humanoid. The experiments demonstrate that the proposed model can efficiently handle several aspects of learning by imitation. We first show that it can be utilized in an unsupervised learning manner, where the robot is autonomously organizing and encoding variants of motion from the multiple demonstrations. We then show that the approach allows to robustly generalize the observed skill by taking into account multiple constraints in task space during reproduction.