Papers

Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids

Achieving robust humanoid hiking in complex, unstructured environments requires transitioning from reactive proprioception to proactive perception. However, integrating exteroception remains a significant challenge: mapping-based methods suffer from state estimation drift; for instance, LiDAR-based methods do not handle torso jitter well. Existing end-to-end approaches often struggle with scalability and training complexity; specifically, some previous works using virtual obstacles are implemented case-by-case. In this work, we present Hiking in the Wild, a scalable, end-to-end parkour perceptive framework designed for robust humanoid hiking. To ensure safety and training stability, we introduce two key mechanisms: a foothold safety mechanism combining scalable Terrain Edge Detection with Foot Volume Points to prevent catastrophic slippage on edges, and a Flat Patch Sampling strategy that mitigates reward hacking by generating feasible navigation targets. Our approach utilizes a single-stage reinforcement learning scheme, mapping raw depth inputs and proprioception directly to joint actions, without relying on external state estimation. Extensive field experiments on a full-size humanoid demonstrate that our policy enables robust traversal of complex terrains at speeds up to 2.5 m/s. The training and deployment code is open-sourced to facilitate reproducible research and deployment on real robots with minimal hardware modifications.

HumanoidHiking

Shaoting Zhu, Ziwen Zhuang, Mengjie Zhao · 982 views · Jan 12, 2026

CReF: Cross-modal and Recurrent Fusion for Depth-conditioned Humanoid Locomotion

CReF is a single-stage depth-conditioned humanoid locomotion framework that maps onboard proprioception and forward-facing depth directly to joint position targets, without explicit geometric intermediates. It couples proprioception and depth tokens via proprioception-queried cross-modal attention, fuses them with a gated residual block, and integrates temporal context with a GRU regulated by a highway-style output gate. A terrain-aware foothold placement reward extracts supportable candidates from foot-end point-cloud windows and rewards touchdowns near them. Full CReF leads all terrain categories and difficulty levels in simulation and transfers zero-shot to a physical AGIBOT X2 Ultra across handrail stairs, hollow pallets, reflective interference, and cluttered outdoor scenes.

HumanoidLocomotion

Yuan Hao, Ruiqi Yu, Shixin Luo · 1.3k views · Mar 31, 2026

RoboGesture: Real-Time Semantic-aligned Co-Speech Gestures Generation for Humanoid Interaction

Enabling humanoid robots to respond to human speech with synchronized and semantically meaningful gestures is fundamental to natural human-robot interaction. However, this task faces three critical barriers: the scarcity of semantically rich datasets, the "modality eclipse" where models ignore audio cues in favor of kinematic inertia, and the sim-to-real gap regarding physical safety. We propose RoboGesture, a robot-centric framework that co-designs data, modeling, and control to power a complete interactive human-humanoid system in which the robot listens, responds, and gestures in real time. We first establish the RoboGesture dataset featuring over 300 gesture categories and develop an automated pipeline to synthesize large-scale collision-free, robot-specific audio-motion pairs. Our architecture features a Hierarchical Semantic-Acoustic Aligner that extracts multi-granular prosodic and semantic cues directly from raw audio tokens. These cues drive a Streaming Conditional Motion Generator based on a diffusion transformer with conditional flow matching. To ensure high responsiveness, we introduce Anti-Inertia CFG Masking, which prevents the model from collapsing into repetitive historical patterns by compelling it to proactively mine control signals from the audio modality. Finally, an MPC-based safety filter ensures real-time, collision-free execution on physical hardware. Experiments on a Unitree G1 humanoid demonstrate that RoboGesture generates safer, more rhythmic, and more semantically appropriate responses compared to state-of-the-art baselines.

MPCSim-to-Real

Wang, Zifan, Ren, Ziang, Shi, Pengyang · 1.5k views · Aug 27, 2026

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.

Diffusion policiesResidual RL

Yan Wu, Chenhao Li, Kaifeng Zhao · 1.1k views · Sep 1, 2026

PAMoR: Parameterized Affective Motion Generation in Real Time for Humanoid Robots

People read a humanoid robot's motion in social settings not only for the action performed but for the affect conveyed. Motion carrying that affect has so far been generated for human avatars, where style is taken from a reference clip or an emotion word, neither of which can be quantitatively parameterized. We present PAMoR, which turns affect into a measured control parameter: a valence-arousal (V-A) coordinate computed natively on robot kinematics. It is obtained in closed form from postural expansion and movement energy, and these measurements serve directly as generation conditions, with no human annotation. An action prior and two affect priors, trained in a shared latent space, are composed at each denoising step: the action prior fixes what is performed, the affect priors modulate how. Whole-body motion rolls out autoregressively on a 29-DoF Unitree G1 in real time, with action and affect both editable. Generated motion tracks the commanded V-A over its full range while text-to-motion fidelity still matches text-only baselines. In a perceptual study, raters identify the commanded emotion on 0.38 of trials, above both baselines and approaching the 0.44 reported for acted human bodies.

Unitree G1Humanoid robots

Yan Pan, Lingfan Bao, Tianhu Peng · 795 views · Aug 28, 2026

ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control

Achieving robust, human-like whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittle process of tuning controllers. We introduce ZEST (Zero-shot Embodied Skill Transfer), a streamlined motion-imitation framework that trains policies via reinforcement learning from diverse sources – high-fidelity motion capture, noisy monocular video, and non-physics-constrained animation – and deploys them to hardware zero-shot. ZEST generalizes across behaviors and platforms while avoiding contact labels, reference or observation windows, state estimators, and extensive reward shaping. Its training pipeline combines adaptive sampling, which focuses training on difficult motion segments, and an automatic curriculum using a model-based assistive wrench, together enabling dynamic, long-horizon maneuvers. We further provide a procedure for selecting joint-level gains from approximate analytical armature values for closed-chain actuators, along with a refined model of actuators. Trained entirely in simulation with moderate domain randomization, ZEST demonstrates remarkable generality. On Boston Dynamics' Atlas humanoid, ZEST learns dynamic, multi-contact skills (e.g., army crawl, breakdancing) from motion capture. It transfers expressive dance and scene-interaction skills, such as box-climbing, directly from videos to Atlas and the Unitree G1. Furthermore, it extends across morphologies to the Spot quadruped, enabling acrobatics, such as a continuous backflip, through animation. Together, these results demonstrate robust zero-shot deployment across heterogeneous data sources and embodiments, establishing ZEST as a scalable interface between biological movements and their robotic counterparts.

ZESTSim-to-Real

Jean Pierre Sleiman, He Li, Alphonsus Adu-Bredu · 891 views · Jan 30, 2026

Humanoid Seated Locomotion on Passive Mobile Chair

Research on omnidirectional seated locomotion for humanoids on passive chairs. Policies use proprioception only, outperforming standing in tracking. Analyzes regularization effects on convergence.

HumanoidSeated Locomotion

Kango Yanagida, Kazuki Miyazawa, Takato Horii · 493 views · Aug 28, 2026

LAC: Linear and Angular Compliance for Humanoid Whole-body Control

LAC is a general humanoid whole-body controller that simultaneously realizes commanded linear and angular compliance for wrenches applied to the upper body. Sampled force and couple events imposed on contact frames from human interaction data synthesize a large-scale compliant-response dataset; teacher-student RL then trains a single policy to track compliant motions under external wrenches, with sim and real experiments showing monotonic stiffness modulation and teleoperated loco-manipulation.

HumanoidWhole-Body Control

Yang Liu, Zhongkai Gu, Wei Zhu · 787 views · Aug 26, 2026

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.

HumanoidWorld Models

Ziyang Cheng, Tianshu Tang, Jinxin Lan · 649 views · Aug 18, 2026

Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data

Human athletes demonstrate versatile and highly-dynamic tennis skills to successfully conduct competitive rallies with a high-speed tennis ball. However, reproducing such behaviors on humanoid robots is difficult, partially due to the lack of perfect humanoid action data or human kinematic motion data in tennis scenarios as reference. In this work, we propose LATENT, a system that Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa. The imperfect human motion data consist only of motion fragments that capture the primitive skills used when playing tennis rather than precise and complete human-tennis motion sequences from real-world tennis matches, thereby significantly reducing the difficulty of data collection. Our key insight is that, despite being imperfect, such quasi-realistic data still provide priors about human primitive skills in tennis scenarios. With further correction and composition, we learn a humanoid policy that can consistently strike incoming balls under a wide range of conditions and return them to target locations, while preserving natural motion styles. We also propose a series of designs for robust sim-to-real transfer and deploy our policy on the Unitree G1 humanoid robot. Our method achieves surprising results in the real world and can stably sustain multi-shot rallies with human players. Project page: https://zzk273.github.io/LATENT/

LATENTSim-to-Real

Zhikai Zhang, Haofei Lu, Yunrui Lian · 688 views · Mar 13, 2026

T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion

Achieving both anthropomorphic naturalness and robust terrain traversal remains a fundamental challenge in humanoid locomotion. Existing Reinforcement Learning (RL) approaches typically rely on fixed motion priors, limiting their adaptability to varying environments. We propose Terrain-conditioned Generative Motion Priors (T-GMP), a module that captures a terrain-conditioned latent motion manifold from a few expert state-terrain demonstrations using a Conditional Variational Autoencoder (CVAE). The learned priors enable smooth style transitions, facilitating a unified policy that adapts to terrain variations. We integrate T-GMP into an adversarial learning pipeline with our proposed Foothold Penalty, where a discriminator dynamically modulates naturalness constraints conditioned on local terrain features, guiding the generation of versatile and human-like motions. Experimental results demonstrate that our method outperforms existing baselines in traversal success rate and motion smoothness, while preserving biomimetically natural and physically coordinated motions.

CVAERL

Junhong Guo, Hao Hu, Chen Chen · 587 views · Jun 5, 2026

RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains

RPL uses a two-stage framework that distills terrain-specific height-map experts into a unified depth-based transformer policy, enabling robust bidirectional humanoid locomotion with payloads on slopes, stairs, and stepping stones.

Unitree G1Humanoid

Yuanhang Zhang, Younggyo Seo, Juyue Chen · 592 views · Feb 3, 2026

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