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Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer

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arxiv 2404.05695 v2 pith:YHM4IMBA submitted 2024-04-08 cs.RO cs.AIcs.LGcs.SYeess.SY

Xinyang Gu , Yen-Jen Wang , Jianyu Chen This is my paper · ORCID
classification cs.ROcs.AIcs.LGcs.SYeess.SY
keywords humanoidhumanoid-gymframeworkrobottransferzero-shotenvironmentisaac
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Humanoid-Gym is an easy-to-use reinforcement learning (RL) framework based on Nvidia Isaac Gym, designed to train locomotion skills for humanoid robots, emphasizing zero-shot transfer from simulation to the real-world environment. Humanoid-Gym also integrates a sim-to-sim framework from Isaac Gym to Mujoco that allows users to verify the trained policies in different physical simulations to ensure the robustness and generalization of the policies. This framework is verified by RobotEra's XBot-S (1.2-meter tall humanoid robot) and XBot-L (1.65-meter tall humanoid robot) in a real-world environment with zero-shot sim-to-real transfer. The project website and source code can be found at: https://sites.google.com/view/humanoid-gym/.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Handroid: Bridging Dexterous Hand and Humanoid

    cs.RO 2026-07 conditional novelty 7.0 of 10

    A single 27-DoF body doubles as an anthropomorphic dexterous hand and a 0.33 m desktop humanoid, with a unified control stack for manipulation, locomotion, and embodiment switching.

  2. KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A robot control method that adaptively tightens motion-tracking reward tolerances achieves lower tracking errors on dynamic skills and transfers zero-shot to a real humanoid.

  3. RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control

    cs.RO 2025-06 reject novelty 6.0 of 10

    RLPF uses reinforcement learning with a physics-simulator tracking reward and an alignment verification module to fine-tune a large text-to-motion model for physically feasible humanoid motions.

  4. H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A hierarchical framework that maps wrist force/torque into velocity commands and then into stable leg motions lets a humanoid robot carry loads cooperatively with a human using only haptic cues.

  5. Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking

    cs.RO 2024-12 conditional novelty 6.0 of 10

    Mimicking-Bench provides six humanoid-scene interaction tasks with 23K human motion references and a retarget-track-imitate pipeline that beats data-free RL on average success.

  6. Learning from Massive Human Videos for Universal Humanoid Pose Control

    cs.RO 2024-12 conditional novelty 6.0 of 10

    Humanoid-X contributes 163,800 text-annotated motion clips retargeted from human videos into humanoid robot poses, and UH-1 is an autoregressive transformer that maps text instructions to humanoid actions.

  7. PRISM: Polynomial Representations for Interaction-Structured Motor Control

    cs.RO 2026-07 conditional novelty 5.0 of 10

    Explicit low-degree factorized polynomial proprioceptive features improve robot RL and imitation policies beyond matched-capacity MLPs and induce sensorless compliance-like contact behavior in simulation.

  8. Learning Roller-Skating Motions of Humanoid Robots Based on Adversarial Motion Priors

    cs.RO 2026-07 conditional novelty 5.0 of 10

    Independent AMP-PPO pipelines from retargeted mocap learn Pump Glide and Push Glide on a passive-wheel humanoid, with simulation metrics and real-robot trials.

  9. GBC: Generalized Behavior-Cloning Framework for Whole-Body Humanoid Imitation

    cs.RO 2025-08 conditional novelty 5.0 of 10

    GBC unifies MoCap retargeting and imitation learning into one framework that trains whole-body humanoid policies across multiple robot morphologies in simulation.

  10. Learning to Evaluate Autonomous Behaviour in Human-Robot Interaction

    cs.RO 2025-07 conditional novelty 5.0 of 10

    A neural behavior classifier trained on teleoperated joint trajectories is proposed and tested as an offline meta-evaluator for imitation learning policies in human-robot interaction.

  11. Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics Motions

    cs.RO 2025-02 conditional novelty 5.0 of 10

    A whole-body reinforcement-learning controller, trained only in simulation, lets a Unitree G1 humanoid perform extreme contact-agnostic motions such as getting up from the ground and breaking-dance moves in the real world.

  12. Learning Humanoid Locomotion with Perceptive Internal Model

    cs.RO 2024-11 conditional novelty 5.0 of 10

    A perception-conditioned internal model lets humanoid robots climb 15 cm stairs and cross gaps with around 90% reported success, using onboard elevation maps.

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