Papers

Social Humanoid: Socially Responsive Human-Humanoid Interaction

Humanoid robots are typically trained as solitary agents, leaving them unable to coordinate physically with human partners who naturally anticipate and adjust to one another. To bridge this gap, we present Social Humanoid, a hierarchical system that generates and executes socially responsive whole-body motion on a 29-DoF Unitree G1 conditioned on a human partner's motion. Its high-level policy performs flexible sequence modeling of human and humanoid motion through a shared tokenized representation, generating reactive humanoid motion and anticipating the partner's motion when they are occluded, while a low-level whole-body tracking controller executes the generated motion on the robot. To train the system, we introduce a two-phase adaptive retargeting procedure that retargets one interaction partner to the robot while morphing the other partner to preserve contact dynamics, reachability, and relative body scale. We apply this procedure across five motion-capture datasets to produce a new Human-Humanoid Interaction (HHI) benchmark of 260 hours of human-humanoid interactions. Our results demonstrate that Social Humanoid produces coordinated, human-aligned behavior across diverse interaction types in simulation and the real world. Project page: https://von31.github.io/social-humanoid/

HumanoidHuman-humanoid interaction

Vongani H. Maluleke, James Ni, Kie Horiuchi · 45 views · Nov 5, 2026

HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness

Embodied navigation requires an agent to ground instructions or object goals in spatial observations and turn plans into execution. HarnessVLN is a zero-shot, training-free agent harness that coordinates perception, hierarchical event memory, a persistent spatiotemporal graph, and navigation tools through a shared interface. Before dispatch, the harness checks evidential support, geometric feasibility, and subgoal consistency; structured execution feedback then updates task progress, spatial evidence, and failure records for subsequent planning, recovery, and stopping. HarnessVLN reaches success rates of 60.8% on R2R, 53.9% on RxR, 76.0% on HM3D-v2, and 59.3% on HM3D-OVON, outperforming the training-free methods compared in the paper. Three real-world scenarios on an AgiBot A3U humanoid demonstrate deployment feasibility across both navigation families. Project page: https://agibot-harnessvln.netlify.app/.

Vision-language navigationEmbodied AI

Yang Chen, Lirong Che, Zhenyu Huang · 105 views · Sep 14, 2026

Chunk-wise Model-based Policy Optimization

Model-based reinforcement learning has shown superior sample efficiency by leveraging the learned latent dynamics either in the form of derivative-free planning/imagined rollout training or dynamics gradient-based policy updates. While these approaches achieve comparatively fast learning, they all suffer, to varying degrees, from issues of recurrent compounding errors, high inference-time latency, and instability in backward propagation through time (BPTT). In this work, we propose a model-based RL approach that uses the chunk world model as a differentiable training-time evaluator to optimize the chunk actor without recurrent model invocation. Through the collaborative chunk-wise prediction of the world model and policy, we can generate H-step hypothetical rollouts in a single forward pass and directly optimize value expansion, avoiding the costly and unstable BPTT. We evaluate our method on 19 challenging high-dimensional humanoid tasks from HumanoidBench and deploy our method to the real-world for quadruped bipedal walking. Project Website: https://song-wx.github.io/cmpo.github.io/

HumanoidModel-Based RL

Weixi Song, Donglin Wang, Yong-Lu Li · 147 views · Oct 10, 2026

Movens-Zero: Scaling Humanoid Motion Foundation Models with Large-Scale Human Videos

Movens-Zero builds five million executable humanoid motions from Internet human videos, unifies text, video, speech, and music conditioning, and drives Unitree G1 in real time through chunked closed-loop control.

HumanoidHumanoid Motion Foundation Model

Wendong Bu, Tinghong Ye, Qizhou Wang · 164 views · Oct 30, 2026

METATRON: A Promptable Whole-Body Execution Layer for Humanoids

Humanoid controllers often require task-specific training despite sharing the need for balance and whole-body coordination. We present Metatron, a promptable whole-body execution layer combining a generative base policy with a programmable skill layer. The base policy receives partial goals, collectively called a prompt, that can combine timed body-part targets, velocity commands, and style cues. It produces diverse whole-body motions consistent with these goals. Training combines motion capture, synthetic recovery trajectories, and retargeted scene-interaction motions. After training, the policy is frozen. Stateful programs called skills express task intent as prompts at each control step, drawing rough geometric sketches that the policy turns into coordinated movement. Skills are ordinary code, refined by coding agents that analyze motion data and fit compact rules without task-specific policy training. On the physical Unitree G1, one frozen checkpoint supports walking, recovery, teleoperated object pickup, and sitting on furniture. Comparative evaluations show higher reaching success and push robustness than the tested reference-tracking pipelines. The same recipe produces climbing and acrobatics on a simulated character. Metatron enables coding agents to turn learned motor capabilities into new skills through code, without further policy training.

HumanoidWhole-Body Control

Chen Tessler, Yifeng Jiang, Alec Otto · 451 views · Oct 27, 2026

BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion

BeyondMimic first masters diverse agile humanoid skills with a compact motion-tracking formulation, then composes and guides them through a unified latent diffusion model for unseen tasks and zero-shot deployment.

HumanoidMotion Tracking

Qiayuan Liao, Takara E. Truong, Xiaoyu Huang · 158 views · Aug 11, 2025

Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads

When a humanoid lifts an unknown payload, abrupt changes in mass, center of mass, and inertia destabilize whole-body control. The paper couples centroidal MPC, adaptive fuzzy balance compensation driven by capture-point error, and triple-integrator posture-transition planning. MPC produces motion references under contact, friction, and joint constraints; fuzzy compensation allocates corrective torques to stance legs; and the planner generates smooth, feasible sit-to-stand and stand-to-sit trajectories. MuJoCo simulation and a 28-DoF humanoid validate stable lifting with 7 kg and 10 kg payloads and robust 7 kg load-carrying during walking.

HumanoidWhole-Body Control

Manh Cuong Nguyen, Duc Cuong Vu, Viet Anh Vu · 218 views · Oct 9, 2026

Learning Multi-Phase Robot Loco-Manipulation via Phase-Conditioned Policy Optimization

Complex whole-body loco-manipulation requires executing diverse maneuvers, such as crouching, grasping, and locomotion, within a single long-horizon task. While performing simple pick-and-place behaviors is well-established, scaling to complex, long-horizon tasks remains difficult. Currently, this challenge is addressed by relying on human motion data and correspondingly humanoid platforms, which limits platform independence and introduces substantial data processing overhead. Instead, we extend proximal policy optimization (PPO) to train a single policy for multi-phase tasks across diverse robotic hardware, leveraging prior task structure to shape behavior. Our method adapts PPO using a phase-conditioned critic, phase-gated reward functions, and a multi-phase reset curriculum to guide exploration. To support varied hardware, we employ a decoupled hierarchical control architecture that separates lower-level balancing dynamics from high-level phase execution. We demonstrate multi-stage loco-manipulation skills on hardware on both the Unitree G1 and a Boston Dynamics Spot equipped with a manipulator arm, and validate our design choices through ablations. Our approach enables the acquisition of complex loco-manipulation behaviors purely through environment interaction while directly incorporating high-level task structure into policy learning.

Whole-Body Loco-ManipulationPhase-Conditioned PPO

Simone Manni, Annika Wollschläger, Dario Bellicoso · 291 views · Oct 26, 2026

MorphMimic: Morphology-Conditioned World Model and Policy Sharing Across Humanoids

MorphMimic conditions a shared differentiable world model and tracking policy on a 125-value morphology descriptor, enabling three humanoids to learn from 128,786 common motion clips and share one controller.

HumanoidCross-Embodiment

Lizhi Yang, Fengze Xie, Yuyang Wang · 421 views · Oct 25, 2026

Contact-UAN: Unsupervised Actuator Networks as a Residual World Model for Humanoid Control

Humanoid policies trained in simulation can transfer qualitatively to hardware while failing to transfer the force interactions learned during training, compromising torque efficiency, load regulation, and robustness to impacts. This failure can arise from actuator sim-to-real gaps because forces depend on actuator torques. We introduce Contact-UAN, a residual world model that preserves a simulator's physics priors while correcting actuator dynamics to align simulated rollouts with recorded hardware rollouts. For humanoid motion with contact, this requires contact-consistent replay: if hardware playback in simulation begins with misaligned contact wrenches, the residual model learns to correct reset artifacts rather than actuator discrepancies. Using only onboard proprioception, Contact-UAN infers the simulator-side stance-foot pose needed to initialize replay with consistent contact forces. We show that a UAN trained on 6.5 minutes of real-world walking data generalizes to tasks that benefit from accurate force and impulse regulation and produces policies that transfer seamlessly to the real world, yielding 20% lower running torque, 43% lower force-tracking error, extreme compliance, accurate impedance control, and dynamic, robust jumps.

HumanoidActuator Modeling

Nolan Fey, Khai Nguyen, Yash Prabhu · 256 views · Oct 13, 2026

Compliant GMT: Making General Motion Tracking Compliant via Residual Reference Adaptation

We propose Compliant GMT (C-GMT), a modular framework for full-body compliant responses in general motion tracking. As humanoid robots move into real world deployment, they not only need to perform any motion but also respond safely and robustly to interaction forces. Existing general motion tracking policies can imitate diverse motions but are trained to be stiff, leading to unexpected and unsafe behaviors under disturbance. Prior work in compliant humanoid control trades off compliance and generality. There exist examples of full-body compliant responses, but these are heavily curated to single-reference policies. On the other hand, general motion trackers (GMT) are trained to rigidly imitate massive motion capture datasets. Our key insight is to leverage the generality of pretrained GMTs for motion tracking while introducing compliance from a residual adapter that conditions on a history of proprioception. This enables our approach, C-GMT, to achieve both general motion tracking and whole-body compliant responses. Compared to state-of-the-art general motion trackers and single-motion compliant policies, C-GMT demonstrates competitive performance in both motion tracking accuracy and compliance adherence. We further demonstrate improved safety, robustness, and error tolerance in simulation and real-world G1 experiments.

HumanoidMotion Tracking

Venki Parsuram, Michelle Wang, Jin-Hyuk Hong · 290 views · Oct 15, 2026

From Video to Vision: Learning Generalizable Humanoid Carrying from Human Motion

Most video-driven humanoid imitation policies still need an upper-level module to generate reference trajectories at deployment, and the resulting controllers often depend on global position in a motion-capture studio. MOVER replaces that dependency with a three-stage teacher-assistant-student pipeline. It first recovers human motion from 100 videos with GENMO, retargets it to a 29-DoF Tiangong 3.0 humanoid through GMR, and augments object geometry, scale, and pose to obtain 4,000 reference trajectories. A teacher policy learns strict motion and object tracking; an assistant relaxes global body-position tracking to improve placement generalization; and a DAgger-PPO student removes proprioceptive position and reference-joint-position inputs. At deployment, a head-mounted RGB-D camera and an object tag provide relative object pose, while QuatNet predicts the reference anchor orientation from object pose, joint state, and target location. The paper evaluates four object placements and two shape-size configurations in simulation, then demonstrates carrying on the physical robot without motion capture. Remaining limitations include limited demonstration diversity, imperfect target localization without global proprioception, and a small real-world trial count.

HumanoidObject Carrying

Ziluo Ding · 284 views · Oct 10, 2026

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