Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal

TL;DR

This paper presents a hierarchical, experience-informed navigation framework for quadrupedal robots to traverse complex rebar environments, combining high-level planning with kinodynamically-aware low-level motion optimization, validated in simulation and real hardware.

Contribution

It introduces a novel layered contact planning approach that integrates experience heuristics and torso path guidance for improved navigation success in complex terrains.

Findings

Experience heuristic reduces offline trials needed.

Torso path guidance improves obstacle navigation.

Validated on real quadrupedal robot.

Abstract

This study focuses on a layered, experience-based, multi-modal contact planning framework for agile quadrupedal locomotion over a constrained rebar environment. To this end, our hierarchical planner incorporates locomotion-specific modules into the high-level contact sequence planner and solves kinodynamically-aware trajectory optimization as the low-level motion planner. Through quantitative analysis of the experience accumulation process and experimental validation of the kinodynamic feasibility of the generated locomotion trajectories, we demonstrate that the experience planning heuristic offers an effective way of providing candidate footholds for a legged contact planner. Additionally, we introduce a guiding torso path heuristic at the global planning level to enhance the navigation success rate in the presence of environmental obstacles. Our results indicate that the torso-path guided experience accumulation requires significantly fewer offline trials to successfully reach the goal compared to regular experience accumulation. Finally, our planning framework is validated in both dynamics simulations and real hardware implementations on a quadrupedal robot provided by Skymul Inc.