I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning

Mon, 05 Oct 2026 00:00:00 +0000

Equal contribution: Ziqi Han, Yitang Li and Junhan Sun.

One behavior space for interaction

I-BFM learns a shared representation of humanoid motion, object motion, and contact through unsupervised reinforcement learning. Downstream rewards are converted into latent commands, allowing one policy to perform different interaction tasks without optimizing a new task-specific policy.

Robust execution beyond nominal motion

Short-horizon interaction targets and longer-horizon goals support carrying, pushing, kicking, and task chaining. The work evaluates recovery after falls and external disturbances, alongside hardware loco-manipulation on a Unitree G1. The reported simulation Carry success is 94.3% under nominal conditions and 89.3% after robot falls in the evaluated benchmark.

The overview separates representation pretraining from downstream reward inference. Project videos provide the corresponding simulation and real-robot demonstrations.

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Behavioral Foundation Models | Raymon Yip CV

I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning

One behavior space for interaction

Robust execution beyond nominal motion