# SoftMimic: Learning Compliant Whole-body Control from Examples arXiv: https://arxiv.org/abs/2510.17792 ยท HTML: https://mlsanity.com/paper/2510.17792/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. - Builds on 25 of its 58 references that are in the map. - First posted on arXiv 20 Oct 2025. ## Placed in - Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/) ## Builds on (most cited first, 25 of 25) - [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) - [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021) - [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017) - [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019) - [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024) - [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021) - [HumanPlus: Humanoid Shadowing and Imitation from Humans](https://mlsanity.com/paper/2406.10454/) (2024) - [Rapid Locomotion via Reinforcement Learning](https://mlsanity.com/paper/2205.02824/) (2022) - [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025) - [Expressive Whole-Body Control for Humanoid Robots](https://mlsanity.com/paper/2402.16796/) (2024) - [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022) - [Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation](https://mlsanity.com/paper/2403.04436/) (2024) - [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022) - [TWIST: Teleoperated Whole-Body Imitation System](https://mlsanity.com/paper/2505.02833/) (2025) - [GMT: General Motion Tracking for Humanoid Whole-Body Control](https://mlsanity.com/paper/2506.14770/) (2025) - [MuJoCo Playground](https://mlsanity.com/paper/2502.08844/) (2025) - [HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit](https://mlsanity.com/paper/2502.13013/) (2025) - [KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills](https://mlsanity.com/paper/2506.12851/) (2025) - [AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control](https://mlsanity.com/paper/2505.03738/) (2025) - [RSL-RL: A Learning Library for Robotics Research](https://mlsanity.com/paper/2509.10771/) (2025) - [FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation](https://mlsanity.com/paper/2505.06776/) (2025) - [Learning Force Control for Legged Manipulation](https://mlsanity.com/paper/2405.01402/) (2024) - [DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation](https://mlsanity.com/paper/2509.04441/) (2025) - [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025) ## Built on by (most cited first, 14 of 14) - [ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data](https://mlsanity.com/paper/2603.09170/) (2026) - [Tune to Learn: How Controller Gains Shape Robot Policy Learning](https://mlsanity.com/paper/2604.02523/) (2026) - [CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation](https://mlsanity.com/paper/2512.14689/) (2025) - [Coordinated Humanoid Manipulation with Choice Policies](https://mlsanity.com/paper/2512.25072/) (2025) - [TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model](https://mlsanity.com/paper/2609.09158/) (2026) - [Fauna Sprout: A lightweight, approachable, developer-ready humanoid robot](https://mlsanity.com/paper/2601.18963/) (2026) - [HybridMimic: Hybrid RL-Centroidal Control for Humanoid Motion Mimicking](https://mlsanity.com/paper/2603.06775/) (2026) - [Constrained Whole-Body Tracking for Humanoid Robots](https://mlsanity.com/paper/2606.00374/) (2026) - [Bicycle Acrobatics with Reinforcement Learning](https://mlsanity.com/paper/2608.00880/) (2026) - [Dreaming the Sound of Contact: Leveraging Video and Audio Generation for Zero-Shot Force-Aware Manipulation and Data Generation](https://mlsanity.com/paper/2609.19137/) (2026) - [Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation](https://mlsanity.com/paper/2609.23968/) (2026) - [Smoothness as a Constraint for Stable Humanoid Locomotion](https://mlsanity.com/paper/2609.24552/) (2026) - [CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video](https://mlsanity.com/paper/2610.05324/) (2026) - [HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids](https://mlsanity.com/paper/2610.08970/) (2026) ## Abstract (arXiv) We introduce SoftMimic, a framework for learning compliant whole-body control policies for humanoid robots from example motions. Imitating human motions with reinforcement learning allows humanoids to quickly learn new skills, but existing methods incentivize stiff control that aggressively corrects deviations from a reference motion, leading to brittle and unsafe behavior when the robot encounters unexpected contacts. In contrast, SoftMimic enables robots to respond compliantly to external forces while maintaining balance and posture. Our approach leverages an inverse kinematics solver to generate an augmented dataset of feasible compliant motions, which we use to train a reinforcement learning policy. By rewarding the policy for matching compliant responses rather than rigidly tracking the reference motion, SoftMimic learns to absorb disturbances and generalize to varied tasks from a single motion clip. We validate our method through simulations and real-world experiments, demonstrating safe and effective interaction with the environment.