# GentleHumanoid: Learning Upper-body Compliance for Contact-rich Human and Object Interaction

arXiv: https://arxiv.org/abs/2511.04679 · HTML: https://mlsanity.com/paper/2511.04679/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning](https://mlsanity.com/paper/2406.08858/) (2024)
- [HumanPlus: Humanoid Shadowing and Imitation from Humans](https://mlsanity.com/paper/2406.10454/) (2024)
- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [TWIST: Teleoperated Whole-Body Imitation System](https://mlsanity.com/paper/2505.02833/) (2025)
- [Inter-X: Towards Versatile Human-Human Interaction Analysis](https://mlsanity.com/paper/2312.16051/) (2023)
- [ExBody2: Advanced Expressive Humanoid Whole-Body Control](https://mlsanity.com/paper/2412.13196/) (2024)
- [GMT: General Motion Tracking for Humanoid Whole-Body Control](https://mlsanity.com/paper/2506.14770/) (2025)
- [PromptHMR: Promptable Human Mesh Recovery](https://mlsanity.com/paper/2504.06397/) (2025)
- [FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation](https://mlsanity.com/paper/2505.06776/) (2025)
- [Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation](https://mlsanity.com/paper/2502.10894/) (2025)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [Adaptive Force-Based Control of Dynamic Legged Locomotion over Uneven Terrain](https://mlsanity.com/paper/2307.04030/) (2023)
- [Hierarchical Optimization-based Control for Whole-body Loco-manipulation of Heavy Objects](https://mlsanity.com/paper/2311.00112/) (2023)
- [Hierarchical Adaptive Loco-manipulation Control for Quadruped Robots](https://mlsanity.com/paper/2209.13145/) (2022)
- [SPARK: Safe Protective and Assistive Robot Kit](https://mlsanity.com/paper/2502.03132/) (2025)
- [Humanoid Loco-Manipulations Pattern Generation and Stabilization Control](https://mlsanity.com/paper/2505.24116/) (2025)

## Built on by (most cited first, 7 of 7)

- [Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations](https://mlsanity.com/paper/2601.09518/) (2026)
- [CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation](https://mlsanity.com/paper/2512.14689/) (2025)
- [ECHO: Edge-Cloud Humanoid Orchestration for Language-to-Motion Control](https://mlsanity.com/paper/2603.16188/) (2026)
- [SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction](https://mlsanity.com/paper/2606.27581/) (2026)
- [Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement](https://mlsanity.com/paper/2609.13236/) (2026)
- [Constrained Whole-Body Tracking for Humanoid Robots](https://mlsanity.com/paper/2606.00374/) (2026)
- [CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video](https://mlsanity.com/paper/2610.05324/) (2026)

## Abstract (arXiv)

Humanoid robots are expected to operate in human-centered environments where safe and natural physical interaction is essential. However, most recent reinforcement learning (RL) policies emphasize rigid tracking and suppress external forces. Existing impedance-augmented approaches are typically restricted to base or end-effector control and focus on resisting extreme forces rather than enabling compliance. We introduce GentleHumanoid, a framework that integrates impedance control into a whole-body motion tracking policy to achieve upper-body compliance. At its core is a unified spring-based formulation that models both resistive contacts (restoring forces when pressing against surfaces) and guiding contacts (pushes or pulls sampled from human motion data). This formulation ensures kinematically consistent forces across the shoulder, elbow, and wrist, while exposing the policy to diverse interaction scenarios. Safety is further supported through task-adjustable force thresholds. We evaluate our approach in both simulation and on the Unitree G1 humanoid across tasks requiring different levels of compliance, including gentle hugging, sit-to-stand assistance, and safe object manipulation. Compared to baselines, our policy consistently reduces peak contact forces while maintaining task success, resulting in smoother and more natural interactions. These results highlight a step toward humanoid robots that can safely and effectively collaborate with humans and handle objects in real-world environments.