Learning Geometric Constraints in Task and Motion Planning

TL;DR

This paper introduces a method to learn and transfer geometric constraints in task and motion planning using constraint primitives and Bayesian optimization, significantly reducing exploration costs.

Contribution

It proposes a novel approach combining semantic and geometric backtracking with Bayesian optimization for efficient constraint learning and transfer in TAMP.

Findings

Reduces exploration calls by up to 71.69%

Uses transferable primitives for geometric constraints

Guides binding search effectively with Bayesian optimization

Abstract

Searching for bindings of geometric parameters in task and motion planning (TAMP) is a finite-horizon stochastic planning problem with high-dimensional decision spaces. A robot manipulator can only move in a subspace of its whole range that is subjected to many geometric constraints. A TAMP solver usually takes many explorations before finding a feasible binding set for each task. It is favorable to learn those constraints once and then transfer them over different tasks within the same workspace. We address this problem by representing constraint knowledge with transferable primitives and using Bayesian optimization (BO) based on these primitives to guide binding search in further tasks. Via semantic and geometric backtracking in TAMP, we construct constraint primitives to encode the geometric constraints respectively in a reusable form. Then we devise a BO approach to efficiently utilize the accumulated constraints for guiding node expansion of an MCTS-based binding planner. We further compose a transfer mechanism to enable free knowledge flow between TAMP tasks. Results indicate that our approach reduces the expensive exploration calls in binding search by 43.60to 71.69 when compared to the baseline unguided planner.