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

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 hierarchical learning framework for partner-agnostic intent-aware collaborative legged transport that represents partner intent as an explicit interaction wrench and recovers it from payload-coupled proprioceptive histories. PAINT decouples intent understanding from terrain-robust locomotion: A high-level policy uses the inferred interaction wrench for transport and reconstructs it through teacher-student training, 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 can reuse the same single-agent policy for decentralized multi-robot transport by mechanically combining team interactions into a common wrench space, and supports heterogeneous-team transport. Our results suggest that payload-coupled proprioceptive interaction provides a physically grounded interface for partner-agnostic intent-aware collaborative transport.

Robotics (cs.RO)

arXiv:2604.12852 [cs.RO]

(or arXiv:2604.12852v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.12852 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/LRA.2026.3730304 Focus to learn more DOI(s) linking to related resources

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