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.

翻译：摘要：协同运输要求机器人通过物理交互推断合作伙伴意图，同时保持稳定的运动操控能力。在复杂环境中，交互信号难以捕捉和建模，这使得该任务特别具有挑战性。我们提出了PAINT——一种轻量级但高效的分层学习框架，用于实现与合作伙伴无关的意图感知型腿式协同运输。该框架直接从本体感觉反馈中推断合作伙伴意图。PAINT将意图理解与地形鲁棒运动解耦：高层策略通过意图估计器和师生训练方案推断合作伙伴交互力矩，而低层运动主干网络确保稳健执行。这实现了无需外部力/力矩传感或负载跟踪的轻量级部署。大量仿真和真实世界实验表明，该框架能在多种地形、负载和合作伙伴条件下实现顺应性协同运输。此外，通过替换底层运动主干网络，PAINT可自然扩展到去中心化多机器人运输并跨机器人构型迁移。实验结果表明，负载耦合交互中的本体感觉信号为构建合作伙伴无关的意图感知协同运输提供了可扩展接口。