A Vision-Based Learning Method for Pushing Manipulation

We describe an unsupervised on-line method for learning of manipulative actions that allows a robot to push an object connected to it with a rotational point contact to a desired point in image-space. By observing the results of its actions on the object\u27s orientation in image-space, the system forms a predictive forward empirical model. This acquired model is used on-line for manipulation planning and control as it improves. Rather than explicitly inverting the forward model to achieve trajectory control, a stochastic action selection technique [Moore, 1990] is used to select the most informative and promising actions, thereby integrating active perception and learning by combining on-line improvement, task-directed exploration, and model exploitation. Simulation and experimental results of the approach are presented

Sensorimotor Learning Using Active Perception in Continuous Domains

We propose that some aspects of task based learning in robotics can be approached using nativist and constructivist views on human sensorimotor development as a metaphor. We use findings in developmental psychology, neurophysiology, and machine perception to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a dexterous three fingered hand, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be though of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up some important issues such as observation sparsity in high dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for generalization directions determining projections of high dimensional data sets which capture task invariants. Additionally, the learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate grave mistakes during learning and not damage itself. We address this by the use of an instrumented compliant robot wrist which controls impact forces

Robotic Sensorimotor Learning in Continuous Domains

We propose that some aspects of task based learning in robotics can be approached using nativist and constructivist views on human sensorimotor development as a metaphor. We use findings in developmental psychology, neurophysiology, and machine perception to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a dexterous three fingered hand, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up some important issues such as observation sparsity in high dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for generalization directions determining projections of high dimensional data sets which capture task invariants. Additionally, the learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate grave mistakes during learning and not damage itself. We address this by the use of an instrumented compliant robot wrist which controls impact forces

Editorial

Welcome to our regular edition of CIT devoted to the 2008 ITI (Information Technology Interfaces) Conference. The following 15 papers (out of 148) were selected from those originally published in the ITI 2008 Conference Proceedings (IEEE Catalog Number CFP08498-PRT). As in previous years, papers were selected after having received high ratings from both of their respective international reviewers and as being representative of the broad scope character of the ITI Conference in terms of topics solicited over these last 30 years

Acquisition and improvement of human motor skills: Learning through observation and practice

Skilled movement is an integral part of the human existence. A better understanding of motor skills and their development is a prerequisite to the construction of truly flexible intelligent agents. We present MAEANDER, a computational model of human motor behavior, that uniformly addresses both the acquisition of skills through observation and the improvement of skills through practice. MAEANDER consists of a sensory-effector interface, a memory of movements, and a set of performance and learning mechanisms that let it recognize and generate motor skills. The system initially acquires such skills by observing movements performed by another agent and constructing a concept hierarchy. Given a stored motor skill in memory, MAEANDER will cause an effector to behave appropriately. All learning involves changing the hierarchical memory of skill concepts to more closely correspond to either observed experience or to desired behaviors. We evaluated MAEANDER empirically with respect to how well it acquires and improves both artificial movement types and handwritten script letters from the alphabet. We also evaluate MAEANDER as a psychological model by comparing its behavior to robust phenomena in humans and by considering the richness of the predictions it makes

An Assessment of Machine Learning Methods for Robotic Discovery

In this paper we consider autonomous robot discovery through experimentation in the robot’s environment. We analyse the applicability of machine learning (ML) methods with respect to various levels of robot discovery tasks, from extracting simple laws among the observed variables, to discovering completely new notions that were never mentioned in the data directly. We first present some illustrative experiments in robot learning in the XPERO European project. Then we formulate a systematic list of types of learning or discovery tasks, and discuss the suitability of chosen ML methods for these tasks