Bilevel Learning for Dual-Quadruped Collaborative Transportation under Kinematic and Anisotropic Velocity Constraints

TL;DR

This paper introduces a bilevel learning framework enabling dual quadruped robots to collaboratively transport payloads while respecting kinematic and anisotropic velocity constraints, improving coordination and obstacle avoidance.

Contribution

The paper presents a novel bilevel learning approach that jointly optimizes team collaboration and individual robot velocities under complex constraints for dual-quadruped transportation.

Findings

Outperforms baseline methods in challenging scenarios

Effectively handles anisotropic velocity constraints

Enables precise payload transportation with obstacle avoidance

Abstract

Multi-robot collaborative transportation is a critical capability that has attracted significant attention over recent years. To reliably transport a kinematically constrained payload, a team of robots must closely collaborate and coordinate their individual velocities to achieve the desired payload motion. For quadruped robots, a key challenge is caused by their anisotropic velocity limits, where forward and backward movement is faster and more stable than lateral motion. In order to enable dual-quadruped collaborative transportation and address the above challenges, we propose a novel Bilevel Learning for Collaborative Transportation (BLCT) approach. In the upper-level, BLCT learns a team collaboration policy for the two quadruped robots to move the payload to the goal position, while accounting for the kinematic constraints imposed by their connection to the payload. In the lower-level, BLCT optimizes velocity controls of each individual robot to closely follow the collaboration policy while satisfying the anisotropic velocity constraints and avoiding obstacles. Experiments demonstrate that our BLCT approach well enables collaborative transportation in challenging scenarios and outperforms baseline approaches.