#Motion retargeting (32)

RoboSkills Generates Humanoid Motion From Video

@gengchen · 38 views · 2026-11-02

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.

Wendong Bu, Tinghong Ye, Qizhou Wang · 66 views · Oct 30, 2026

HumanoidHumanoid Motion Foundation Model

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.

Chen Tessler, Yifeng Jiang, Alec Otto · 316 views · Oct 27, 2026

HumanoidWhole-Body Control

Oct 27, 2026

0:18

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.

Ziluo Ding · 252 views · Oct 10, 2026

HumanoidObject Carrying

Oct 10, 2026

0:58

GPT-6 Astra Reconstructs Human Sphere Play with a Sharpa Hand

@memmelma · 172 views · 2026-10-23

Dexterous ManipulationMotion retargeting

Humanoid Mobility via Attention-based Contacts

Humanoid mobility over complex terrain requires accurate foot placement and coordinated whole-body motion capable of robust adaptation. Reinforcement learning (RL) policies trained without motion references generalize well across terrains, yet eliciting effective whole-body coordination through reward design and exploration alone remains challenging. In contrast, reference-based methods yield natural motions but struggle to adapt beyond the predefined trajectories in their training data. We introduce a learning framework that bridges these two approaches by using motion data strictly as a training prior. An explicit, self-supervised, attention-based contact planner predicts future body-environment contacts, while a contact-conditioned tracker translates these plans into coordinated whole-body motion. Together, they establish contact as an interface between sensory observations and control actions. This abstraction allows the policy to internalize the contact-aware structure of locomotion rather than directly replicating kinematics. Consequently, a single policy can exploit data-driven motion priors for natural whole-body coordination while retaining the flexibility to discover environment-dependent behaviors through reinforcement learning. Building on the learned contact planner and tracker, a subsequent fine-tuning stage bootstraps their acquired capabilities to explore environment variations beyond those covered by the original motion data. Operating entirely from onboard observations without any reference inputs at deployment, this framework enables versatile humanoid mobility in challenging environments.

Junzhe He, Kehan Wen, Chong Zhang · 377 views · Oct 22, 2026

HumanoidLocomotion

Oct 22, 2026

DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations

Despite recent progress in dexterous reference tracking, existing models focus primarily on single-hand manipulation and a limited range of interactions, leaving the bimanual dexterous behaviors common in real life unaddressed. The scarcity of contact-rich bimanual manipulation data and the increased complexity of coordinating contacts make developing a generalist bimanual tracker particularly challenging. Therefore, we present 1) DONIC, the first generalist bimanual tracker jointly trained across operation categories and human-motion sources, together with 2) BiHOI, a multi-view dataset of 1,575 sequences spanning 229 objects and emphasizing sustained bimanual interaction, where reconstructed interactions are retargeted into robot references. DONIC combines proximal policy optimization with behavioral cloning in a feedback loop that recycles verified successful executions into training demonstrations. An object-centered per-hand reward maintains object-motion feedback while supervising each hand according to its reference contact role. Clip-Centered Advantages remove reference-dependent advantage offsets while preserving within-trajectory differences. Together, these designs enable one single policy to learn coordinated manipulation across heterogeneous references. Without finetuning, a single checkpoint achieves 86.79% success on unseen trajectories of training objects and 57.32% on held-out objects, surpassing the state-of-the-art bimanual tracking baselines by 77.36 and 45.73 percentage points.

Mingze Sun, Ziang Cao, Yuan Fei · 218 views · Oct 21, 2026

Dexterous ManipulationBimanual Manipulation

Oct 21, 2026

Vision as Proprioception: Estimating Humanoid Robot Pose from Video

HumanoidPose combines frozen human-pose features with robot kinematics to recover full-body joint configurations and world-space motion from monocular video across embodiments.

Jiaxi Jiang, Matthias Heyrman, René Zurbrügg · 240 views · Oct 18, 2026

HumanoidPose estimation

Oct 18, 2026

1:17

HumanoidPose Estimates Full-Body Humanoid Pose from Video

@cs_jiaxi_jiang · 133 views · 2026-10-22

HumanoidPose estimation

1:14

SuperMimic Scales Humanoid Control with 1,000x Fewer GPU-Hours

@qiayuanliao · 135 views · 2026-10-21

Humanoid ControlMotion Imitation

PriMoRetarget: Humanoid Motion Transfer Through Prioritized Motion Retargeting

Motion retargeting is a critical preprocessing step for transferring human motion data to humanoid robots. While existing methods are effective for single-character motions, interactions involving multiple characters and objects introduce additional contacts and spatial relationships that must be preserved. Retargeting is commonly formulated as a weighted multi-objective optimization problem, where conflicting objectives can require careful tuning of relative weights and can still compromise important motion characteristics. We introduce PriMoRetarget, a prioritized motion-retargeting framework that replaces relative objective weighting with an explicit priority hierarchy. Lower-priority updates are constrained to preserve higher-priority objectives, allowing critical characteristics such as foot placement and interaction contacts to take precedence over lower-priority pose objectives. The method is evaluated on four datasets spanning single-character motion, character-object manipulation, and character-character interactions. The resulting motions better preserve task-relevant interactions and provide reference trajectories that can be tracked more reliably than those produced by widely used retargeting methods.

Nicholas Ioannidis, Dun Yang, Max Smith · 270 views · Oct 8, 2026

HumanoidMotion retargeting

Oct 8, 2026