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#Residual RL (2)

WaterfallPagination
ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids

ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids

ResSafe decouples performance and safety in humanoid control: a nominal policy optimizes task performance, then a residual RL policy trained on top learns only safety corrections, their sum acting as an implicit min-norm safety filter. On Unitree G1 extreme balance under random payloads, fall rate drops from 7.29% to 1.11% with near-nominal tracking error, generalizing across reference checkpoints and transferring to real hardware.

Qu, Gechen, Zhang, Tong, Zhang, BikeSep 14, 2026
HumanoidSafety FilterResidual RLSep 14, 2026
ADAPT: Agile Diffusion Action Priors for Robust and Steerable Online Text-Driven Humanoid Control

ADAPT: Agile Diffusion Action Priors for Robust and Steerable Online Text-Driven Humanoid Control

We present ADAPT, an end-to-end framework for interactive, text-conditioned humanoid whole-body control. Unlike dominant text-to-motion pipelines that generate kinematic motions for a separate tracker, ADAPT solves language control with an end-to-end closed-loop control framework, where the robot must continuously respond to changing commands while maintaining balance, natural motion, and smooth transitions. ADAPT learns a diffusion-based action prior from text-labeled humanoid state-action trajectories, enabling diverse motion skills to be directly executed from language commands. To improve long-horizon robustness and smooth prompt switching, we train a lightweight residual reinforcement learning policy on top of the frozen diffusion controller. We further show that the same diffusion policy can be reused as a steerable text-conditioned motion prior for downstream task adaptation. Experiments demonstrate robust language-grounded skill execution, smooth interactive transitions, and style-preserving downstream control.

Yan Wu, Chenhao Li, Kaifeng ZhaoSep 1, 2026
Diffusion policiesResidual RLUnitree G1Sep 1, 2026