タスク・動作計画における幾何制約の学習と転移
Learning Geometric Constraints in Task and Motion Planning
TAMPにおける幾何パラメータ探索を、再利用可能な制約プリミティブとベイズ最適化で効率化し、タスク間で知識を転移する手法を提案した。
著者: Tianyu Ren, Alexander Imani Cowen-Rivers, Haitham Bou Ammar, Jan Peters
分類: cs.RO
原文アブストラクト
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.
