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‘Brilliant’: US public cheers dancing Unitree robots while Congress looks to ban them
‘Brilliant’: US public cheers dancing Unitree robots while Congress looks to ban them Eight Unitree G1 humanoid robots performed an elaborate dance routine on America’s Got Talent, alongside a Chinese dancer from Sichuan In the segment that aired on Tuesday evening local time, eight G1 humanoid robots performed an elaborate dance routine alongside Wu Yufei, a Chinese dancer from Sichuan province. The act was an audition for the talent competition show, broadcast by NBC, in which contestants...
NVIDIA's Isaac Gr00t platform gives researchers access to frontier humanoid robotics
NVIDIA's Isaac Gr00t platform gives researchers access to frontier humanoid robotics It uses a nearly 6-foot tall humanoid chassis and tactile five finger hands. As part of his AI-palooza Computex keynote, NVIDIA's Jensen Huang dove into the most relatable form of artificial intelligence: robots. The company announced the new Isaac Gr00t reference design humanoid robot platform that combines a Unitree H2 Plus humanoid robot, Sharpa five-fingered hands and NVIDIA Jetson Thor onboard compute.
Bionic Human-Motion Style Transfer for Physically Executable Whole-Body Control of Humanoid Robots
arXiv:2606.03536v1 Announce Type: new Abstract: Expressive whole-body motion is important for humanoid robots operating in human environments, where robots are expected to move stably while presenting readable and adjustable body behaviors. However, most expressive motions are still obtained from fixed demonstrations or manually designed scripts, making it difficult to reuse a demonstrated style across different motion contents. Inspired by the way human motion styles convey affective and...
ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data
arXiv:2603.09170v2 Announce Type: replace Abstract: Achieving versatile and natural whole-body humanoid interaction control remains challenging due to the high cost of whole-body teleoperation data. We present ZeroWBC, a teleoperation-free framework that learns humanoid whole-body interaction from human egocentric videos paired with synchronized whole-body motion and text annotations. ZeroWBC adopts a generation-then-tracking formulation to tackle the static scene whole-body interaction...
Predictive Style Matching: Natural and Robust Humanoid Locomotion
arXiv:2606.07083v1 Announce Type: new Abstract: Reinforcement learning has become the prevailing approach to humanoid locomotion control: policies transfer reliably from simulation to hardware and recover gracefully from disturbances. Motion quality, however, still lags behind: task-only rewards often converge to stiff, asymmetric gaits, while motion imitation methods improve appearance but become more sensitive to external disturbances because reference signals can oppose the transient...
LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World
arXiv:2606.01458v1 Announce Type: new Abstract: Training vision-language-action (VLA) policies for humanoid loco-manipulation is constrained by the high cost and complexity of collecting human teleoperation demonstrations. VLA policies fine-tuned in simulators have, until now, failed to transfer effectively in humanoid loco-manipulation tasks.
Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots
arXiv:2606.03476v1 Announce Type: new Abstract: Retargeting human motion to humanoid robots is critical for teleoperation, imitation learning and human-robot interaction. However, it remains challenging because of substantial morphological discrepancies between humans and robots, including differences in skeletal topology, limb proportions and degrees of freedom, as well as the scarcity of paired motion data. This paper presents Human2Humanoid, an unsupervised motion retargeting framework...
Flash-WAM: Modality-Aware Distillation for World Action Models
Announce Type: new Abstract: World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control. Step distillation has emerged as the natural remedy, but off-the-shelf methods break down in the joint video-action setting because video and action streams use different SNR-shifted noise schedules and reach training with...
M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking
Announce Type: new Abstract: Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomotion and loco-manipulation. Different tasks rely on distinct motion reference modalities: locomotion primarily depends on coordinated robot joint trajectories, whereas manipulation requires precise end-effector trajectory tracking. Existing methods often overlook the representational...
GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors
arXiv:2606.05160v1 Announce Type: new Abstract: Scaling humanoid loco-manipulation requires robot-compatible demonstrations across diverse objects, whole-body motions, and scene geometries, but teleoperation and motion capture are difficult to scale because each collection depends on physical setups, instrumented actors, and robot operation. We present GRAIL, a digital generation pipeline that remains fully virtual until deployment: it composes 3D assets, simulator-ready scenes, and priors...