PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots

TL;DR

PAINT introduces a hierarchical learning framework enabling legged robots to infer partner intent from proprioception for stable, cooperative transport across various terrains without external sensors.

Contribution

The paper presents PAINT, a novel intent-aware framework that decouples intent inference from locomotion, allowing lightweight, scalable, and embodiment-transferable collaborative transport.

Findings

Successful real-world experiments on diverse terrains and payloads.

PAINT scales to multi-robot transport and transfers across robot types.

Operates without external force sensors or payload tracking.

Abstract

Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation. This becomes particularly challenging in complex environments, where interaction signals are difficult to capture and model. We present PAINT, a lightweight yet efficient hierarchical learning framework for partner-agonistic intent-aware collaborative legged transport that infers partner intent directly from proprioceptive feedback. PAINT decouples intent understanding from terrain-robust locomotion: A high-level policy infers the partner interaction wrench using an intent estimator and a teacher-student training scheme, while a low-level locomotion backbone ensures robust execution. This enables lightweight deployment without external force-torque sensing or payload tracking. Extensive simulation and real-world experiments demonstrate compliant cooperative transport across diverse terrains, payloads, and partners. Furthermore, we show that PAINT naturally scales to decentralized multi-robot transport and transfers across robot embodiments by swapping the underlying locomotion backbone. Our results suggest that proprioceptive signals in payload-coupled interaction provide a scalable interface for partner-agnostic intent-aware collaborative transport.