Cross-Entropy Optimization of Physically Grounded Task and Motion Plans
This work provides a method for robots to generate more reliable and executable task and motion plans by explicitly incorporating physical dynamics and contacts, which is a significant problem for robotics researchers and practitioners.
This paper addresses the challenge of robots performing tasks that require both high-level discrete actions and low-level continuous motions by using a GPU-parallelized physics simulator to account for dynamics and contacts. By employing cross-entropy optimization to sample controller parameters, the approach generates low-cost solutions that can be directly executed by the robot, enabling it to exploit environmental geometry for object manipulation.
Autonomously performing tasks often requires robots to plan high-level discrete actions and continuous low-level motions to realize them. Previous TAMP algorithms have focused mainly on computational performance, completeness, or optimality by making the problem tractable through simplifications and abstractions. However, this comes at the cost of the resulting plans potentially failing to account for the dynamics or complex contacts necessary to reliably perform the task when object manipulation is required. Additionally, approaches that ignore effects of the low-level controllers may not obtain optimal or feasible plan realizations for the real system. We investigate the use of a GPU-parallelized physics simulator to compute realizations of plans with motion controllers, explicitly accounting for dynamics, and considering contacts with the environment. Using cross-entropy optimization, we sample the parameters of the controllers, or actions, to obtain low-cost solutions. Since our approach uses the same controllers as the real system, the robot can directly execute the computed plans. We demonstrate our approach for a set of tasks where the robot is able to exploit the environment's geometry to move an object. Website and code: https://andreumatoses.github.io/research/parallel-realization