RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning

TL;DR

RHH-LGP is a novel planning algorithm that combines receding horizon strategies and heuristics within Logic-Geometric Programming to efficiently solve complex, long-horizon robotic manipulation tasks involving multiple robots and objects.

Contribution

The paper introduces RHH-LGP, a new approach that significantly improves planning efficiency for long-horizon tasks by integrating heuristics and receding horizon methods into logic-geometric programming.

Findings

Order-of-magnitude reduction in planning time

Effective handling of multi-robot and heterogeneous tasks

Robust performance across diverse long-horizon scenarios

Abstract

Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many objects that can be interacted with, planning efficiency becomes even more important. To plan such long-horizon tasks, we present the RHH-LGP algorithm for combined task and motion planning (TAMP). First, we propose a TAMP approach (based on Logic-Geometric Programming) that effectively uses geometry-based heuristics for solving long-horizon manipulation tasks. The efficiency of this planner is then further improved by a receding horizon formulation, resulting in RHH-LGP. We demonstrate the robustness and effectiveness of our approach on a diverse range of long-horizon tasks that require reasoning about interactions with a large number of objects. Using our framework, we can solve tasks that require multiple robots, including a mobile robot and snake-like walking robots, to form novel heterogeneous kinematic structures autonomously. By combining geometry-based heuristics with iterative planning, our approach brings an order-of-magnitude reduction of planning time in all investigated problems.