Granular Loco-Manipulation: Repositioning Rocks Through Strategic Sand Avalanche

TL;DR

This paper introduces DiffusiveGRAIN, a learning-based method enabling multi-legged robots to induce sand avalanches and manipulate obstacles for improved mobility on challenging terrains, validated through extensive trials.

Contribution

The paper presents a novel diffusion-based environment predictor and integrated planning approach for obstacle manipulation during robot locomotion on granular terrains.

Findings

Successfully repositioned rocks in over 65% of trials

Identified significant interference effects among closely-spaced obstacles

Demonstrated the importance of integrated manipulation and locomotion planning

Abstract

Legged robots have the potential to leverage obstacles to climb steep sand slopes. However, efficiently repositioning these obstacles to desired locations is challenging. Here we present DiffusiveGRAIN, a learning-based method that enables a multi-legged robot to strategically induce localized sand avalanches during locomotion and indirectly manipulate obstacles. We conducted 375 trials, systematically varying obstacle spacing, robot orientation, and leg actions in 75 of them. Results show that the movement of closely-spaced obstacles exhibits significant interference, requiring joint modeling. In addition, different multi-leg excavation actions could cause distinct robot state changes, necessitating integrated planning of manipulation and locomotion. To address these challenges, DiffusiveGRAIN includes a diffusion-based environment predictor to capture multi-obstacle movements under granular flow interferences and a robot state predictor to estimate changes in robot state from multi-leg action patterns. Deployment experiments (90 trials) demonstrate that by integrating the environment and robot state predictors, the robot can autonomously plan its movements based on loco-manipulation goals, successfully shifting closely located rocks to desired locations in over 65% of trials. Our study showcases the potential for a locomoting robot to strategically manipulate obstacles to achieve improved mobility on challenging terrains.