Loco-Manipulation Planning for Legged Robots: Offline and Online Strategies

TL;DR

This paper presents a combined offline and online planning pipeline for legged robots to generate and refine loco-manipulation trajectories in dynamic environments, enhancing adaptability and safety during robot operation.

Contribution

It introduces a novel contact space planning approach with real-time local refinement using visual data, tested on humanoid and wheeled robot models.

Findings

Offline planner successfully generates global trajectories in contact space.

Online refinement improves trajectory safety and adaptability.

Method demonstrated on humanoid COMAN+ and CENTAURO robots.

Abstract

The deployment of robots within realistic environments requires the capability to plan and refine the loco-manipulation trajectories on the fly to avoid unexpected interactions with a dynamic environment. This extended abstract provides a pipeline to offline plan a configuration space global trajectory based on a randomized strategy, and to online locally refine it depending on any change of the dynamic environment and the robot state. The offline planner directly plans in the contact space, and additionally seeks for whole-body feasible configurations compliant with the sampled contact states. The planned trajectory, made by a discrete set of contacts and configurations, can be seen as a graph and it can be online refined during the execution of the global trajectory. The online refinement is carried out by a graph optimization planner exploiting visual information. It locally acts on the global initial plan to account for possible changes in the environment. While the offline planner is a concluded work, tested on the humanoid COMAN+, the online local planner is still a work-in-progress which has been tested on a reduced model of the CENTAURO robot to avoid dynamic and static obstacles interfering with a wheeled motion task. Both the COMAN+ and the CENTAURO robots have been designed at the Italian Institute of Technology (IIT).