ECHO: In-Context Behavior foundation model for Humanoid Whole-Body Control

Humanoid behavior foundation models (BFMs) acquire diverse skills through large-scale motion imitation and massively parallel reinforcement learning, yet broad behavioral capability does not ensure precise execution across physical conditions. Without distinguishing the underlying dynamics, policies may favor conservative compromises over environment-specific precision. We introduce ECHO, an in-context BFM that turns interaction experience into environment-specific control, separating general behavioral capability from dynamics-specific execution. Mixed and Annealed Sampling first establishes a behavior prior in a single pretraining stage by balancing targeted practice with broad motion coverage. Building on this prior, Context Representation and Residual Adaptation learns structured dynamics representations from interaction histories to guide residual corrections to the frozen prior, enabling adaptation without test-time parameter updates. Extensive experiments show that ECHO outperforms all baseline methods across diverse motions and physical conditions. Moreover, deployment on the Unitree G1 demonstrates whole-body teleoperation spanning extreme acrobatics and terrain interaction, alongside payload adaptation in manipulation and whole-body control. Page is available at https://echo-bfm.github.io/echoweb/https://echo-bfm.github.io/echoweb/.