Discovering Affordances Through Perception and Manipulation

International audienceConsidering perception as an observation process only is the very reason for which robotic perception methods are to date unable to provide a general capacity of scene understanding. Related work in neuroscience has shown that there is a strong relationship between perception and action. We believe that considering perception in relation to action requires to interpret the scene in terms of the agent's own potential capabilities. In this paper, we propose a Bayesian approach for learning sensorimotor representations through the interaction between action and observation capabilities. We represent the notion of affordance as a probabilistic relation between three elements: objects, actions and effects. Experiments for affordances discovery were performed on a real robotic platform in an unsupervised way assuming a limited set of innate capabilities. Results show dependency relations that connect the three elements in a common frame: affordances. The increasing number of interactions and observations results in a Bayesian network that captures the relationships between them. The learned representation can be used for prediction tasks

Probabilistic Interactive Segmentation for Anthropomorphic Robots in Cluttered Environments

Abstract — Recognition and manipulation of novel objects in human environments are a prerequisite for many tasks of robots. Since objects often occur in clutter, such robots should be capable of segmenting their environment into individual objects before attempting to learn the objects ’ properties. In this paper, we propose a probabilistic part-based approach to interactive segmentation of cluttered scenes containing multiple novel objects. Our experiments show that our probabilistic approach outperforms commonly employed heuristics. Furthermore, the probability distribution over segmentations enables principled selection of informative actions. I