#Unitree G1 (85)

G1 Goalkeeper Turns Live Vision Into Saves

@ChenTessler · 93 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 · 67 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

0:24

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.

Simone Manni, Annika Wollschläger, Dario Bellicoso · 251 views · Oct 26, 2026

Whole-Body Loco-ManipulationPhase-Conditioned PPO

Oct 26, 2026

0:30

BRACE: Whole-Body Force Control on Rough Terrain

@spaceandtech_ · 79 views · 2026-10-27

BRACEUnitree G1

OpenBelief-Nav: Evidence-Preserving Object Memory for Open-Vocabulary Language-Guided Navigation

Open-vocabulary 3D scene graphs provide compact semantic memory for language-guided navigation, but mapped objects are often exposed through a single fused feature or committed semantic label. Such commitment can remove minority yet task-relevant hypotheses from the task-time interface. We present OpenBelief-Nav, an evidence-preserving object memory that retains observation-level phrases, reliability cues, and frame-mask provenance while maintaining separate aggregate geometric and visual representations. Semantically related phrases are consolidated into a vocabulary-independent object belief from which task-specific readouts perform fixed-vocabulary projection or free-form retrieval. On five ScanNet200 and eight Replica scenes, full-belief projection achieves mIoU scores of 0.2742 and 0.2912, compared with 0.2393 and 0.2701 for a matched early-commit readout. Across 78 HM3D-YCB navigation trials, consensus and early-commit retrieval each achieve 60/78 successes, compared with 58/78 for belief-weighted retrieval and 55/78 for DualMap. Across 20 Unitree G1 runs organized as 10 matched evaluation cases, a correction policy permitting at most two verified candidate attempts improves target-confirmation success from 6/10 to 8/10 relative to top-1-only execution. Code will be released upon acceptance at https://openbelief-nav.github.io/.

Dinh Tuan Nguyen, Anh Dao, Phuong Nam Dang · 169 views · Aug 14, 2026

Open-Vocabulary Navigation3D scene graphs

Aug 14, 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.

Nolan Fey, Khai Nguyen, Yash Prabhu · 227 views · Oct 13, 2026

HumanoidActuator Modeling

Oct 13, 2026

0:33

KPI Adds an Interaction Contract to Humanoid Control

@LeoKharon · 118 views · 2026-10-25

HumanoidContact Control

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 · 379 views · Oct 22, 2026

HumanoidLocomotion

Oct 22, 2026

3:01

FIBER: Action-Aligned Latent Planning for Humanoid Loco-Manipulation

@yutang_llin · 175 views · 2026-10-20

HumanoidWhole-Body Control

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

P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning

Behavioral foundation models (BFMs) provide humanoids with reusable repertoires of whole-body behaviors; in real-world humanoid control, the same behavioral intent may require fundamentally different motions under different terrain constraints. Covering such variation with terrain-matched demonstrations requires enumerating a combinatorial space of behaviors and terrain geometries. We introduce P-BFM, a perceptive behavioral foundation model that factorizes humanoid control into terrain-invariant intent and terrain-conditioned realization. A forward-backward representation maps motion references, target states, and reward functions into a shared intent space without terrain information, while a perceptive policy learns how to realize each intent under the surrounding terrain geometry. We further identify a complementary limitation of purely latent control: behavioral latents can preserve local motion patterns while losing precise global progress. We therefore introduce Explicit Root Instructions (ERI), which separates global transport (e.g., heading and planar velocity) from behavioral intent, while leaving local whole-body adaptation to the perceptive policy. This decomposition leads to a characteristic capability absent from conventional BFMs: one fixed intent induces different, terrain-appropriate behaviors, including changes in step height, stride, posture, foothold placement, and contacts, without terrain-specific demonstrations. Across challenging terrains, P-BFM improves motion fidelity, root-control precision, and perturbation recovery over strong baselines. A single frozen policy further supports motion tracking, goal reaching, reward inference, teleoperation, and terrain-aware recovery, and transfers zero-shot to a Unitree G1 using onboard depth sensing.

Xue Wang, Yitang Li, Yiming Zhang · 496 views · Oct 11, 2026

HumanoidUnsupervised Reinforcement Learning

Oct 11, 2026

BRACE: Adapting Whole-Body References for Force and Terrain Aware Humanoid Motion Tracking

Whole-body tracking has become the interface through which operators drive humanoid robots, yet the references it consumes are recorded on level ground and carrying nothing, so the tracker knows neither the forces the robot must exchange with objects nor the terrain it stands on. Existing controllers address one side of the gap: force-capable policies command an end-effector force but prescribe no whole-body pose, while terrain-adaptive trackers treat loads as disturbances to reject rather than wrenches to command. BRACE folds both into the reference the tracker follows: terrain conformance lifts footholds and root onto the local surface, and a wrench transformation resolves the hand displacement that produces a commanded force together with the center-of-mass and center-of-pressure shifts it induces, bounded by teacher-specific arm-effort limits. Separate exertion and compensation teachers are distilled by DAgger into one flow-matching student that runs on proprioception alone, without a height map or measured wrench, so a teleoperator can supply a flat-ground trajectory while the robot resolves slope and load onboard. On the Unitree G1, in simulation and on hardware, BRACE cuts global tracking error by 40% on average and improves success rate by 18.25 points.

Sudarshan Harithas, Chen Yu, Juan Borbon · 335 views · Oct 5, 2026

HumanoidWhole-Body Control

Oct 5, 2026

2:43

BRACE: Force- and Terrain-Aware Whole-Body Tracking for Humanoids

@sudarshan_s_h · 194 views · 2026-10-13

HumanoidWhole-Body Control

0:29

A MuJoCo Sim of a Unitree G1 Visualizes the Zero-Moment Point

@AlloyRobotics · 164 views · 2026-10-14

Zero-Moment PointMuJoCo

1:15

ASENA: A Coding Agent Programs a G1 to Fetch and Hand Over Objects

@anjjei · 193 views · 2026-10-14

ASENACoding Agent

PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface

PanTact treats contact, not joint trajectories, as the transfer currency from people to humanoids. Wearable patches are assembled magnetically from 44x44 mm FSR modules; a custom 50x50 mm PCB streams 16 channels at 1 kHz over a CRC-checked RS-485 daisy chain, one Vive tracker per patch maps sensor centers into the world frame, and contact states are binarized against a per-channel adaptive baseline with threshold max(0.35 beta, r_min). The policy observes body-masked 3D egocentric point clouds, both wrist images and proprioception, and at 10 Hz predicts an action chunk of H=16 steps containing a look-at point, EEF poses with gripper widths, and per-sensor contact goals (binary state plus 3D position). A soft-minimum proximity d(p,q) with T = 0.02 m hands each active contact to whichever robot surface is currently nearest, and active goals are offset by delta along the pressing direction to form virtual goals that generate pressing force, so the G1 holds objects without any tactile sensor on its body. Three control formulations trade off differently: training-free upper-body IK, WBIK whose dense reference is executed by SONIC, and a sparse contact tracker trained with PPO over 4096 simulation environments for 30k iterations (50 Hz, 29 joints, S=8 upper-body / S=16 whole-body contact slots). On a 29-DoF Unitree G1: Grasp-and-Embrace reaches 85% with upper-body IK and 80% with the sparse tracker against 0% for Humanoid UMI; Sweep-and-Stow reaches 90.63% end to end while Humanoid UMI++ scores 18.75% on each stage and 0% end to end; Carry-and-Push is 90% versus 40%. In simulation replay, adding contact goals lowers contact distance for all three controllers at almost no cost in EEF accuracy.

Xu, Xiaomeng, Zhang, Han, Ruan, Zhian · 563 views · Oct 8, 2026

Whole-Body ManipulationTactile Sensing

Oct 8, 2026

HomeBody: A Humanoid That Explores, Remembers, and Acts on Its Own

Stanford TML's HomeBody drops the learned VLA middle layer: GPT Astra calls a library of composable skills on a Unitree G1, backed by a digital twin and persistent spatial memory the robot builds by exploring a home it has never seen, then tidies an unseen kitchen and fetches medicine with no task-specific training.

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Humanoid robotsEmbodied AI

PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control

Diffusion models provide a flexible framework for motion generation, but turning this flexibility into closed-loop humanoid control remains challenging. Hierarchical generator-tracker systems steer motion through reference trajectories, yet these references may exceed the capabilities of the downstream tracker, leaving physical feasibility and disturbance recovery largely to a separate control module. Action-only diffusion avoids this separation by directly generating executable actions, but provides no explicit future-state trajectory that can be steered toward test-time motion objectives. Joint state-action diffusion offers a natural alternative, but existing controllers often rely on privileged full-body states, while learned behavior selection and test-time motion steering remain only partially integrated. We present PredActor, a predictive action diffusion policy that unifies both steering modes in one directly executed policy using proprioception alone. Given proprioceptive history and optional task context, PredActor jointly predicts actions and an internal future-state trajectory that enables guidance: classifier-free guidance strengthens text-conditioned motion, while classifier guidance steers future states toward test-time objectives. Only actions are executed, requiring neither a motion-reference tracker nor privileged full-body states. In simulation, PredActor reaches 44 of 45 destination targets and achieves a text retrieval score of 0.539 versus 0.424 for conditional action diffusion, with similar disturbance survival. Rolling denoising and computation-preserving runtime optimizations reduce the complete callback to 16.790 ms median and 19.383 ms p95 on a Jetson Orin NX, within the 20 ms control period. Deployed on a Unitree G1, PredActor demonstrates text-conditioned motion, disturbance response, joystick control, and semantic interpolation in simulation and hardware.

Ye, Lei, Gao, Haibo, Li, Yitang · 368 views · Sep 21, 2026

Humanoid ControlDiffusion policies

Sep 21, 2026

HuMBLE: Human Motion-Driven Behavior Learning for Embodied Locomotion

Despite recent advances in humanoid locomotion, controllers optimized for command tracking and robustness tend to produce mechanical gaits, whereas controllers tied to human motion data often fail to generalize to commands outside the data distribution. This work introduces a learning framework that balances these competing objectives to synthesize real-time steerable, robust, and biomimetic locomotion policies from human data. Using an in-house curated locomotion dataset covering diverse speeds and directions, we first learn a natural locomotion prior policy through a teacher-student distillation process. Specifically, we train a full-body reference-conditioned policy with Reinforcement Learning (RL), then distill it into a lightweight prior policy conditioned solely on proprioception and a planar torso-velocity steering command. Next, we fine-tune the prior policy with multi-task RL to expand command coverage and robustness beyond the data distribution, pairing a goal-conditioned task that tracks arbitrary commands with a reference-guided task that tracks the human data as an explicit style regularizer. We validate our framework on three humanoid robots: the Boston Dynamics Atlas R1, Atlas D1, and Unitree G1. Experimental results demonstrate robust performance across real-world scenarios, including direct user-controlled locomotion in indoor and outdoor environments, and integration as the locomotion layer within hierarchical control stacks. Benchmarks against Tabula Rasa RL policies trained without human data and ablation studies confirm that our framework yields a lightweight, deployable policy that reconstructs coordinated whole-body behavior from a steering command, retaining the human gait characteristics while remaining robust and fully steerable.

Mike Zhang, Dongho Kang, Kevin Bergamin · 733 views · Oct 7, 2026

Humanoid LocomotionMotion Imitation

Oct 7, 2026

1:40

DexWeave: Learning Dexterous Humanoid Loco-Manipulation from Human Demonstrations

@ErenChenAI · 303 views · 2026-10-09

Mobile ManipulationHumanoid

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#Unitree G1 (85)

G1 Goalkeeper Turns Live Vision Into Saves

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

METATRON: A Promptable Whole-Body Execution Layer for Humanoids

Unitree G1 Parkour Meets a Tilting Ladder

ACP: Browser Playground for Unitree G1 Skills

HULK Teaches Humanoids Whole-Body Control Under Heavy Load

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

BRACE: Whole-Body Force Control on Rough Terrain

OpenBelief-Nav: Evidence-Preserving Object Memory for Open-Vocabulary Language-Guided Navigation

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

KPI Adds an Interaction Contract to Humanoid Control

Humanoid Mobility via Attention-based Contacts

FIBER: Action-Aligned Latent Planning for Humanoid Loco-Manipulation

PriMoRetarget: Humanoid Motion Transfer Through Prioritized Motion Retargeting

P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning

BRACE: Adapting Whole-Body References for Force and Terrain Aware Humanoid Motion Tracking

BRACE: Force- and Terrain-Aware Whole-Body Tracking for Humanoids

A MuJoCo Sim of a Unitree G1 Visualizes the Zero-Moment Point

ASENA: A Coding Agent Programs a G1 to Fetch and Hand Over Objects

PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface

HomeBody: A Humanoid That Explores, Remembers, and Acts on Its Own

PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control

HuMBLE: Human Motion-Driven Behavior Learning for Embodied Locomotion

DexWeave: Learning Dexterous Humanoid Loco-Manipulation from Human Demonstrations