Combining Dynamical Systems Control and Programming by Demonstration for Teaching Discrete Bimanual Coordination Tasks to a Humanoid Robot

We present a generic framework that combines Dynamical Systems movement control with Programming by Demon- stration (PbD) to teach a robot bimanual coordination task. The model consists of two systems: a learning system that processes data collected during the demonstration of the task to extract coordination constraints and a motor system that reproduces the movements dynamically, while satisfy- ing the coordination constraints learned by the ¯rst system. We validate the model through a series of experiments in which a robot is taught bimanual manipulatory tasks with the help of a human.

Learning Dynamical System Modulation for Constrained Reaching Tasks

Aude Billard, Sylvain Calinon, Micha Hersch

In this paper we combine kinesthetic demonstrations and dynamical systems to enable a humanoid robot to imitate constrained reaching gestures directed toward a target. Using a learning algorithm based on Gaussian Mixture Regression, the task constraints are extracted from several demonstrations. Those constraints take the form of desired velocity profiles of the end-effector and joint angle variables. The velocity profiles are then used to modulate a dynamical system which has the reaching target as attractor. This way, the reaching trajectory can be reshaped in order to satisfy the constraints of the task, while preserving the adaptability and robustness provided by the dynamical system. In particular, the system can adapt to changes in the initial conditions and to target displacements occurring during the movement execution. We first evaluate the potential of this method on experiments involving the Hoap3 humanoid robot putting an object into a box. We then show how a manipulation tasks can be executed as sequences of such constrained reaching movement. This is illustrated on a packaging task performed by the robot.

2006

Learning of Gestures by Imitation in a Humanoid Robot

Aude Billard, Sylvain Calinon

In this chapter, we explore the issue of encoding, recognizing, generalizing and reproducing arbitrary gestures. We address one major and generic issue, namely how to discover the essence of a gesture, i.e. how to find a representation of the data that encapsulates only the key aspects of the gesture, and discards the intrinsic variability across people motions. The model is tested and validated in a humanoid robot, using kinematics data of human motion. In order for the robot to learn new skills by imitation, it must be endowed with the ability to generalize over multiple demonstrations. To achieve this, the robot must encode multivariate time-dependent data in an efficient way. Principal Component Analysis and Hidden Markov Models are used to reduce the dimensionality of the dataset and to extract the primitives of the motion. The model takes inspiration in a recent trend of research that aims at defining a formal mathematical framework for imitation learning. In particular, it stresses the fact that the observed elements of a demonstration, and the organization of these elements should be stochastically described to have a robust robotic application. It bears similarities with theoretical models of animal imitation, and offers at the same time a probabilistic description of the data, more suitable for a real-world application.

Cambridge University Press2007

Enactive robot vision

Mototaka Suzuki

The complexity of today's autonomous robots poses a major challenge for Artificial Intelligence. These robots are equipped with sophisticated sensors and mechanical abilities that allow them to enter our homes and interact with humans. For example, today's robots are almost all equipped with vision and several of them can move over rough terrain with wheels or legs. The methods developed so far in Artificial Intelligence, however, are not yet ready to cope with the complexity of the information gathered through the robot sensors and the need for rapid action in partially unknown and dynamic environments. In this thesis, I will argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same time scale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. This line of work is grounded on philosophical and psychological research showing that perception is an active process mediated by behavior. However, computational models of active vision are very rare and often rely on architectures that are preprogrammed to detect certain characteristics of the environment. Previous work have shown that complex visual tasks, such as position and size invariant shape recognition as well as navigation, can be tackled with remarkably simple neural architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons were evolved while freely interacting with their environments. I proceed further on this line of investigation and describe the application of this methodology in three situations, namely car driving with an omnidirectional camera, goal-oriented navigation of a humanoid robot, and cooperative tasks by two agents. I will show that these systems develop sensitivity to a number of oriented, retinotopic, visual features – oriented edges, corners, height – and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system that allow them to accomplish their goals. In a second set of experiments, I will show that active vision can be exploited by the robot to perform anticipatory exploration of the environment in a task that requires landmark-based navigation. Evolved robots exploit an internal expectation system that makes use of active exploration to check for expected events in the environment. I will describe a third set of experiments where, in addition to an evolutionary process, the visual system of the robot can develop receptive fields by means of unsupervised Hebbian learning and show that these receptive fields are significantly affected by the behavior of the system and differ from those predicted by most computational models of visual cortex. Finally, I will show that these robots replicate the performance deficiencies observed in experiments of motor deprivation with kitten when they are exposed to the same type of motor deprivations. Furthermore, the analyses of our robot brains suggest an explanation for the deficiencies observed in kitten that have not yet been fully understood.

EPFL2007

Enactive robot vision

Mototaka Suzuki

EPFL2007