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#Fall Recovery (3)

WaterfallPagination
GigaBrain-WBC-0.5: A Behavior World Model for Robust Whole-Body Control with Environment Interaction

GigaBrain-WBC-0.5: A Behavior World Model for Robust Whole-Body Control with Environment Interaction

The first Behavior World Model (BWM) for humanoid whole-body control. A causal Transformer jointly predicts next action, state, and latent behavior command distribution, enabling the policy network to model how the environment shapes actions. Automatic terrain-annotation pipeline recovers 3D contact geometry from retargeted motion. At deployment, implausible commands are detected and retracted onto learned behaviors. Achieves highest success rate across all four regimes: 81.3% terrain interaction, 83.1% under implausible commands, 99.3% fall recovery. Unitree G1 checkpoint transfers to Maker L01 robot.

Ziyang Cheng, Tianshu Tang, Jinxin LanAug 18, 2026
HumanoidWorld ModelsWhole-Body ControlAug 18, 2026
UFO: Unsupervised Fall Recovery for Humanoids
0:49

UFO: Unsupervised Fall Recovery for Humanoids

@LeoKharon · 93 views · 2026-08-01
HumanoidFall RecoveryUFO
Learning Humanoid Standing-up Control across Diverse Postures

Learning Humanoid Standing-up Control across Diverse Postures

Standing-up control is crucial for humanoid robots, with the potential for integration into current locomotion and loco-manipulation systems, such as fall recovery. Existing approaches are either limited to simulations that overlook hardware constraints or rely on predefined ground-specific motion trajectories, failing to enable standing up across postures in real-world scenes. To bridge this gap, we present HoST (Humanoid Standing-up Control), a reinforcement learning framework that learns standing-up control from scratch, enabling robust sim-to-real transfer across diverse postures. HoST effectively learns posture-adaptive motions by leveraging a multi-critic architecture and curriculum-based training on diverse simulated terrains. To ensure successful real-world deployment, we constrain the motion with smoothness regularization and implicit motion speed bound to alleviate oscillatory and violent motions on physical hardware, respectively. After simulation-based training, the learned control policies are directly deployed on the Unitree G1 humanoid robot. Our experimental results demonstrate that the controllers achieve smooth, stable, and robust standing-up motions across a wide range of laboratory and outdoor environments. Videos and code are available at https://taohuang13.github.io/humanoid-standingup.github.io/.

Tao Huang, Junli Ren, Huayi WangFeb 12, 2025
Sim-to-RealUnitree G1Fall RecoveryFeb 12, 2025