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Unitree G1
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unitree-g1-2773f983·6 events·first seen Aliases: Unitree G1, Unitree G1-Edu
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PAC-MAN: Perception-Aware CBF-RL Framework for Whole-Body Humanoid Dodgeball with 95% Real-World Success
Researchers present PAC-MAN, a framework combining control barrier functions (CBF) with reinforcement learning for whole-body safety in humanoid robots, demonstrated via a dodgeball evasion task. The system uses only onboard depth camera with semantic segmentation, no privileged state information, and is deployed zero-shot on a Unitree G1 humanoid achieving 95% dodge success on real throws. The work advances perception-aware safety for humanoid locomotion by showing that fixed onboard cameras are adequate for reactive whole-body evasion when paired with appropriate barrier structure.
DEED framework bridges lab-to-store gap for humanoid retail robots using data-efficient VLA post-training
Researchers present DEED (Data-Efficient Post-Training and Experience-Driven Learning), a systems-level framework for deploying Vision-Language-Action humanoid robots in real retail environments. The approach is evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid and NVIDIA's GR00T N1.6 foundation model, combining data-efficient post-training, experience-driven refinement adapted from RECAP, and a latent-space analysis tool. Key finding: bridging lab-to-real performance gaps is primarily a systems integration challenge rather than an architectural one, achievable with careful data design on a single GPU.
Adaptive safety margins for visual robot navigation via context-conditioned safety critic
A new arXiv paper proposes a context-conditioned safety critic that learns adaptive clearance preferences for ranking diffusion-based trajectory proposals in indoor robot navigation. The critic decomposes into safety, efficiency, and distance-constraint matching terms, trained with privileged ESDF geometry in simulation and distilled into a perception-only selector via teacher-student learning. The method achieves top success rate and SPL on PointGoal navigation benchmarks (HM3D, MP3D) and transfers zero-shot to a Unitree G1 humanoid robot without task-specific tuning.
VLK: Synthetic vision-language-kinematics supervision enables humanoid loco-manipulation from egocentric observations
Researchers introduce a pipeline that generates 48,000 paired vision-language-kinematics trajectories synthetically using 3D Gaussian Splatting to reconstruct indoor scenes, bypassing the need for expensive human-annotated robot data. A VLK policy trained on this data predicts whole-body kinematic trajectories from egocentric images and language instructions, which a whole-body tracker converts to physical actions. The approach is validated on a Unitree G1 humanoid performing navigation and object transport, demonstrating viable sim-to-real transfer for perception-based loco-manipulation.
CoorDex: Learning pipeline for continuous dexterous humanoid loco-manipulation with high-DoF hands
CoorDex is a reinforcement learning pipeline that enables humanoid robots to perform dexterous manipulation while walking, eliminating the stop-and-go pattern common in prior work. The approach trains separate privileged motion tracking teachers for body and hand, distills them into latent priors, and uses coordinated residual RL to compose them for downstream tasks. Demonstrated on a Unitree G1 humanoid with a 20-DoF WUJI hand, the system achieves non-stop bottle grasping, fridge door opening, and cube manipulation in motion. Ablations show that naive joint-space or monolithic approaches fail under the same reward budget, validating the latent-prior architecture.
HANDOFF: Unified humanoid whole-body controller distilled from complementary specialist teachers
HANDOFF is a single whole-body controller for humanoid robots that uses a compact, explicit command-space interface bridging task planning and motor control. It is trained via multi-teacher KL distillation into a mixture-of-experts student from three specialists: whole-body motion tracking, locomotion, and fall-recovery. Evaluated on the Unitree G1, it matches state-of-the-art velocity tracking and demonstrates natural-language-driven task execution via a VLM-based agentic planner without task-specific fine-tuning. The work is relevant to the AI/robotics intersection as it shows a practical path to deploying language-driven agentic planners on physical humanoid hardware.