# Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration

arXiv: https://arxiv.org/abs/2412.15166 · HTML: https://mlsanity.com/paper/2412.15166/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
- [XIRL: Cross-embodiment Inverse Reinforcement Learning](https://mlsanity.com/paper/2106.03911/) (2021)
- [XSkill: Cross Embodiment Skill Discovery](https://mlsanity.com/paper/2307.09955/) (2023)
- [Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation](https://mlsanity.com/paper/2402.19432/) (2024)
- [PMP: Learning to Physically Interact with Environments using Part-wise Motion Priors](https://mlsanity.com/paper/2305.03249/) (2023)
- [Combining Learning-based Locomotion Policy with Model-based Manipulation for Legged Mobile Manipulators](https://mlsanity.com/paper/2201.03871/) (2022)
- [Sim-to-Real Learning for Humanoid Box Loco-Manipulation](https://mlsanity.com/paper/2310.03191/) (2023)
- [Mirage: Cross-Embodiment Zero-Shot Policy Transfer with Cross-Painting](https://mlsanity.com/paper/2402.19249/) (2024)
- [Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation](https://mlsanity.com/paper/2308.09179/) (2023)
- [Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects](https://mlsanity.com/paper/2205.05960/) (2022)
- [Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space Alignment](https://mlsanity.com/paper/2406.01968/) (2024)
- [SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer](https://mlsanity.com/paper/2306.12677/) (2023)
- [BiRP: Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration](https://mlsanity.com/paper/2307.05933/) (2023)

## Built on by (most cited first, 1 of 1)

- [Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion](https://mlsanity.com/paper/2511.14178/) (2025)

## Abstract (arXiv)

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios requiring strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious process. Given the rapid introduction of new humanoid platforms, a cross-embodiment framework that allows generalizable skill transfer is becoming increasingly critical. To address this, we propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype and bypassing the need for re-training on every new robot platform. The model learns behavior primitives from human demonstrations through adversarial imitation, and the complex robot structures are decomposed into functional components, each trained independently and dynamically coordinated. Task generalization is achieved through a human-object interaction graph, and skills are transferred to different robots via embodiment-specific kinematic motion retargeting and dynamic fine-tuning. Our framework is validated on five humanoid robots with diverse configurations, demonstrating stable loco-manipulation and highlighting its effectiveness in reducing data requirements and increasing the efficiency of skill transfer across platforms.