# HumanPlus: Humanoid Shadowing and Imitation from Humans
**arXiv:** 2406.10454
**Authors:** Zipeng Fu, Qingqing Zhao, Qi Wu, Gordon Wetzstein, Chelsea Finn
**Fetched:** 2026-02-13
**Type:** Research Paper

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## Abstract

One of the key arguments for humanoid robots is that they can leverage the vast human data for learning. Yet, turning this hope into reality is challenging. We present HumanPlus, a full-stack system for humanoids to learn motion and autonomous skills from human data. We first train a low-level policy in simulation via reinforcement learning using existing 40-hour human motion datasets. This policy is then transferred to a real humanoid robot, and enables it to track human body and hand movements in real-time using only an RGB camera, a process we call "shadowing." Using shadowing, an operator can teleoperate the humanoid robot to collect whole-body data for learning different tasks in the real world. We then train skill policies using behavior cloning with egocentric vision, enabling autonomous task completion through human imitation. We demonstrate the system on a customized 33-degree-of-freedom, 180-cm humanoid on tasks including wearing a shoe to stand up and walk, unloading objects from warehouse racks, folding a sweatshirt, rearranging objects, typing, and greeting another robot with 60-100% success rates using up to 40 demonstrations.

## Key Contributions

- **Full-stack learning pipeline:** A complete system going from human motion data to autonomous humanoid skill execution — motion learning, real-time shadowing, teleoperation data collection, and behavior cloning
- **Low-level motion policy:** Trained via RL on 40 hours of existing human motion data, enabling humanoid-to-human motion tracking
- **RGB-only shadowing:** Real-time body and hand motion tracking using only a monocular RGB camera — no specialized motion capture hardware required
- **Zero-shot sim-to-real transfer:** Simulation-trained policies transfer directly to real hardware for practical teleoperation
- **Behavior cloning from demonstrations:** Skill policies trained with egocentric vision enable autonomous task completion from as few as 40 demonstrations
- **Diverse task demonstrations:** Successfully demonstrated on 6 real-world tasks (shoe wearing, warehouse unloading, sweatshirt folding, object rearrangement, typing, robot greeting) with 60-100% success rates

## G1 Relevance

HumanPlus demonstrates a paradigm highly applicable to the Unitree G1: learning from human demonstrations via teleoperation shadowing. While the paper uses a custom 33-DOF humanoid, the methodology (RL-based motion imitation, RGB-camera-only tracking, behavior cloning) is transferable to the G1's morphology. The approach of using human motion data at scale for training, combined with minimal sensor requirements (single RGB camera), makes it practical for G1 deployment. The demonstrated tasks (object manipulation, locomotion, warehouse operations) align well with the G1's target applications.

## References

- arXiv: https://arxiv.org/abs/2406.10454