Test-Time Compute for Humanoid Behavior Foundation Models
Humanoid Behavior Foundation Models (BFMs) can execute impressive motions, but downstream applications such as loco-manipulation demand high whole-body positional accuracy.
ProofPaper ↗
Key points
- We show that additional test-time compute can improve execution without retraining the BFM.
- First, we introduce Test-Time Command Optimization (TTCO), which adapts the input command to the specific BFM to produce an executed trajectory closer to the desired motion.
- TTCO finds such commands by sampling-based optimization through closed-loop simulations of the BFM and robot dynamics.
- Simulation and real-robot experiments show that these labels are effective for training high-level loco-manipulation policies.
Sources (1)
- [1]Test-Time Compute for Humanoid Behavior Foundation ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 01:19 PM
Humanoid Behavior Foundation Models (BFMs) can execute impressive motions, but downstream applications such as loco-manipulation demand high whole-body positional accuracy.
We show that additional test-time compute can improve execution without retraining the BFM.
Extractive summary: sentences quoted from the sources.
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