让机器人通过身体感知推断伙伴意图，实现稳定协作搬运。
PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots
- 用本体感觉信号直接推断合作方意图，不依赖外部传感器。
- 在多种地形和负载下实现柔顺协作运输，无需力矩传感。
- 可扩展至多机器人协作，换不同腿式机器人也能用。
协同搬运要求机器人通过物理交互推断伙伴意图，同时保持稳定的运动与操作。这在复杂环境中尤为困难，因交互信号难以捕捉与建模。我们提出PAINT，一种轻量级、高效的分层学习框架，实现对合作方意图的无依赖感知，直接从本体感觉反馈中推断意图。PAINT将意图理解与抗地形运动解耦：高层策略利用意图估计器和师生训练机制推断合作交互力矩，低层运动基干确保鲁棒执行。该设计支持无需外部力矩传感器或负载追踪的轻量化部署。大量仿真与真实实验表明，PAINT可在多样地形、负载与合作方下实现柔顺协作运输。此外，我们验证其自然扩展至去中心化多机器人运输，并可通过更换底层运动基干实现跨机器人形态迁移。结果表明，负载耦合交互中的本体感觉信号为无依赖意图感知协作运输提供了可扩展接口。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。