Learn about a new planning framework to help make robots regular helpers in our daily lives

Presented by CS Faculty Candidate Neil Dantam

Robots offer the potential to become regular helpers in our daily lives, yet challenges remain for complex autonomy in human environments. We address the challenge of complex autonomy by automating robot programming. Many useful robot tasks combine discrete decisions about objects and actions with continuous decisions about collision-free motion. We introduce a new planning framework that reasons over the combined logical and geometric space in which the robot operates. By grounding this planning framework in formal language and automata theory, we achieve not only efficient performance but also verifiable operation. Finally, such a rigorously grounded framework offers a firm base to scale to large domains, handle uncertainty in the environment, and incorporate behaviors learned from humans.

Dr. Neil Dantam is a Postdoctoral Research Associate in Computer Science at Rice University. Neil's research focuses on robot planning and control, particularly for manipulation. He has developed methods to combine discrete and geometric planning, improve Cartesian control, and analyze discrete robot policies. In addition, he has contributed to the practical implementation of real-time robot software, supporting collaborations with industry partners at Atlas Copco and the multi-university DRC-Hubo team. Neil received a Ph.D. in Robotics from Georgia Tech and B.S. degrees in Computer Science and Mechanical Engineering from Purdue.