Collaborative Research: A Dynamic Bayesian Approach to Real-Time Estimation and Filtering in Grasp Acquisition and Other Contact Tasks

Robots cannot currently grasp objects or perform other contact tasks in unstructured environments with speed or reliability. This project is developing techniques for accurate real-time perception in support of contact tasks. In the proposed method, sensor data tracks the continuous motions of manipulated objects, while models of the objects are simultaneously updated. Particle filtering, a kind of Monte-Carlo simulation, ensures consistency of this tracking and updating. The strongest impact of this work will be in robotic grasping and manipulation. Because of the synthesis of modeling and probabilistic inference, further impacts can be expected, for example in real-time haptics for telepresence.