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

• URL: http://arxiv.org/abs/2604.12852v1
• Date: Tue, 14 Apr 2026 15:06:38 GMT
• Title: PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots
• Abstract summary: Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation.<n>We present a lightweight yet efficient hierarchical learning framework for partner-agonistic intent-aware collaborative legged transport that infers partner intent directly from proprioceptive feedback.<n>Our results suggest that proprioceptive signals in payload-coupled interaction provide a scalable interface for partner-agnostic intent-aware collaborative transport.
• Score: 29.217220708128025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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