Cross-Entropy Optimization of Physically Grounded Task and Motion Plans

2025-12-12

AI Summary

Task and Motion Planning (TAMP) for robotics suffers from a disconnect between high-level logical reasoning and low-level physical executability; existing approaches, due to oversimplified dynamics and contact modeling, yield plans with limited feasibility and success rates on real robotic systems. Method: We propose an end-to-end joint optimization framework that tightly couples Cross-Entropy Method (CEM) with GPU-accelerated parallel physics simulation (PyBullet/Isaac Gym), embedding contact-aware task-motion parameterization directly within closed-loop motion controllers. Contribution/Results: Unlike conventional methods relying on abstract assumptions, our approach enforces strict physical feasibility and realistic environmental interaction while maintaining computational efficiency. It enables geometry-driven manipulation behaviors—including pushing, sliding, and jamming—across diverse object manipulation scenarios. The framework achieves >92% plan execution success rate and supports direct deployment on real robots without post-processing.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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

Problem

• Optimizes robot task and motion plans using physics simulation
• Addresses dynamics and contacts ignored by previous TAMP methods
• Ensures plans are executable with real low-level controllers

Innovation

• GPU-parallelized physics simulator for plan realizations
• Cross-entropy optimization samples controller parameters for low-cost solutions
• Explicitly accounts for dynamics and environmental contacts in planning