P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning

Key points

• Behavioral foundation models (BFMs) provide humanoids with reusable repertoires of whole-body behaviors; in real-world humanoid control, the same behavioral intent may require fundamentally different motions under different terrain constraints.1
• We further identify a complementary limitation of purely latent control: behavioral latents can preserve local motion patterns while losing precise global progress.1
• We therefore introduce Explicit Root Instructions (ERI), which separates global transport (e.g., heading and planar velocity) from behavioral intent, while leaving local whole-body adaptation to the perceptive policy.1
• Across challenging terrains, P-BFM improves motion fidelity, root-control precision, and perturbation recovery over strong baselines.1

Sources (1)

• [1]P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 06:35 PM

  We introduce P-BFM, a perceptive behavioral foundation model that factorizes humanoid control into terrain-invariant intent and terrain-conditioned realization.

  Behavioral foundation models (BFMs) provide humanoids with reusable repertoires of whole-body behaviors; in real-world humanoid control, the same behavioral intent may require fundamentally different motions under different terrain constraints.

Extractive summary: sentences quoted from the sources.