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

2026-04-14

AI Summary

This work addresses the challenge legged robots face in accurately inferring human or robotic partner intent during collaborative carrying tasks using only proprioceptive sensing, a limitation exacerbated by the common reliance of existing approaches on external force/torque sensors. To overcome this, the authors propose a hierarchical learning framework that decouples intent estimation from terrain-adaptive locomotion by combining a high-level intent estimator with a low-level universal motion control backbone. Leveraging a teacher–student paradigm, the method enables partner-agnostic intent recognition solely from proprioception, eliminating the need for force/torque sensors or explicit payload tracking. The approach supports decentralized multi-robot scalability and cross-platform transferability. Extensive simulations and real-world experiments demonstrate its capability to achieve compliant collaborative transport across diverse terrains, varying payloads, and heterogeneous partners.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• collaborative transport
• intent inference
• legged robots
• physical interaction
• loco-manipulation

Innovation

• proprioceptive feedback
• intent estimation
• legged locomotion
• hierarchical learning
• partner-agnostic collaboration