Logic-Skill Programming: An Optimization-based Approach to Sequential Skill Planning

TL;DR

This paper introduces Logic-Skill Programming (LSP), an optimization-based method for sequencing learned robot skills to solve complex, long-horizon tasks using a novel value function approximation and symbolic search.

Contribution

LSP formulates skill sequencing as an optimization problem and employs tensor train factorization for value function approximation, enabling effective planning in complex manipulation tasks.

Findings

LSP outperforms state-of-the-art reinforcement learning in reward approximation.

LSP successfully plans skill sequences in three manipulation domains.

Real-robot experiments confirm robustness to contact uncertainty and disturbances.

Abstract

Recent advances in robot skill learning have unlocked the potential to construct task-agnostic skill libraries, facilitating the seamless sequencing of multiple simple manipulation primitives (aka. skills) to tackle significantly more complex tasks. Nevertheless, determining the optimal sequence for independently learned skills remains an open problem, particularly when the objective is given solely in terms of the final geometric configuration rather than a symbolic goal. To address this challenge, we propose Logic-Skill Programming (LSP), an optimization-based approach that sequences independently learned skills to solve long-horizon tasks. We formulate a first-order extension of a mathematical program to optimize the overall cumulative reward of all skills within a plan, abstracted by the sum of value functions. To solve such programs, we leverage the use of tensor train factorization to construct the value function space, and rely on alternations between symbolic search and skill value optimization to find the appropriate skill skeleton and optimal subgoal sequence. Experimental results indicate that the obtained value functions provide a superior approximation of cumulative rewards compared to state-of-the-art reinforcement learning methods. Furthermore, we validate LSP in three manipulation domains, encompassing both prehensile and non-prehensile primitives. The results demonstrate its capability to identify the optimal solution over the full logic and geometric path. The real-robot experiments showcase the effectiveness of our approach to cope with contact uncertainty and external disturbances in the real world.