Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery

• URL: http://arxiv.org/abs/2510.22336v2
• Date: Wed, 05 Nov 2025 15:01:29 GMT
• Title: Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery
• Abstract summary: We propose RoboCraft, a scalable humanoid co-design framework for fall recovery.<n>A shared policy pretrained across multiple designs is progressively finetuned on high-performing morphologies.<n>Experiments show that RoboCraft achieves an average performance gain of 44.55% on seven public humanoid robots.
• Score: 55.951691393378354
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Humanoid robots represent a central frontier in embodied intelligence, as their anthropomorphic form enables natural deployment in humans' workspace. Brain-body co-design for humanoids presents a promising approach to realizing this potential by jointly optimizing control policies and physical morphology. Within this context, fall recovery emerges as a critical capability. It not only enhances safety and resilience but also integrates naturally with locomotion systems, thereby advancing the autonomy of humanoids. In this paper, we propose RoboCraft, a scalable humanoid co-design framework for fall recovery that iteratively improves performance through the coupled updates of control policy and morphology. A shared policy pretrained across multiple designs is progressively finetuned on high-performing morphologies, enabling efficient adaptation without retraining from scratch. Concurrently, morphology search is guided by human-inspired priors and optimization algorithms, supported by a priority buffer that balances reevaluation of promising candidates with the exploration of novel designs. Experiments show that RoboCraft achieves an average performance gain of 44.55% on seven public humanoid robots, with morphology optimization drives at least 40% of improvements in co-designing four humanoid robots, underscoring the critical role of humanoid co-design.

Related papers

• BRIDGE: An Open-Source Humanoid Platform via Morphology-Control Co-Design for Physical AI [38.7271333345027]
  Bridge is an open-source, 88cm-tall humanoid platform released alongside its control policy.<n>We demonstrate that Bridge captures human motion data with superior fidelity, exhibiting exceptional performance across locomotion, robust balance, and highly dynamic maneuvers.
  arXiv  Detail & Related papers  (2026-09-03T08:01:19Z)
• Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots [9.096721498763717]
  Retargeting human motion to humanoid robots is critical for teleoperation, imitation learning and human-robot interaction.<n>This paper presents Human2Human, an unsupervised motion framework that transfers human motions to humanoid robot behaviors with high fidelity.<n> Experimental results show that the proposed method successfully retargets human motion to the Unitree G1 humanoid robot without paired data.
  arXiv  Detail & Related papers  (2026-06-02T10:53:06Z)
• EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration [67.13034606664333]
  EgoHumanoid is the first framework to co-train a vision-language-action policy using egocentric human demonstrations.<n>A portable system for scalable human data collection is developed.
  arXiv  Detail & Related papers  (2026-02-10T18:59:03Z)
• Scalable and General Whole-Body Control for Cross-Humanoid Locomotion [47.617416398266506]
  We introduce XHugWBC, a novel cross-embodiment training framework for humanoid robots.<n>XHugWBC internalizes a broad distribution of morphological and dynamical characteristics during training.<n> Experiments on twelve simulated humanoids and seven real-world robots demonstrate the strong generalization and robustness of the resulting universal controller.
  arXiv  Detail & Related papers  (2026-02-05T15:48:15Z)
• FRoM-W1: Towards General Humanoid Whole-Body Control with Language Instructions [147.04372611893032]
  We present FRoM-W1, an open-source framework designed to achieve general humanoid whole-body motion control using natural language.<n>We extensively evaluate FRoM-W1 on Unitree H1 and G1 robots.<n>Results demonstrate superior performance on the HumanML3D-X benchmark for human whole-body motion generation.
  arXiv  Detail & Related papers  (2026-01-19T07:59:32Z)
• ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning [59.64325421657381]
  Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks.<n>We introduce ResMimic, a two-stage residual learning framework for precise and expressive humanoid control from human motion data.<n>Results show substantial gains in task success, training efficiency, and robustness over strong baselines.
  arXiv  Detail & Related papers  (2025-10-06T17:47:02Z)
• OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction [76.44108003274955]
  A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning policies.<n>We introduce OmniRetarget, an interaction-preserving data generation engine based on an interaction mesh.<n>By minimizing the Laplacian deformation between the human and robot meshes, OmniRetarget generates kinematically feasible trajectories.
  arXiv  Detail & Related papers  (2025-09-30T17:59:02Z)
• STRIDE: Automating Reward Design, Deep Reinforcement Learning Training and Feedback Optimization in Humanoid Robotics Locomotion [33.91518509518502]
  We introduce STRIDE, a novel framework built on agentic engineering to automate reward design, DRL training, and feedback optimization for humanoid robot locomotion tasks.<n>By combining structured principles of agentic engineering with large language models (LLMs) for code-writing, zero-shot generation, and in-context optimization, STRIDE generates, evaluates, and iteratively refines reward functions without relying on task-specific prompts or templates.<n>Across diverse environments featuring humanoid robot morphologies, STRIDE outperforms the state-of-the-art reward design framework EUREKA, achieving an average improvement of round 250% in
  arXiv  Detail & Related papers  (2025-02-07T06:37:05Z)
• HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation [50.616995671367704]
  We present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands. Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies.
  arXiv  Detail & Related papers  (2024-03-15T17:45:44Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction [5.501477817904299]
  We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints. It is tested on a human arm kinematic model and implemented on a human-robot collaborative setup with a UR5 robot arm.
  arXiv  Detail & Related papers  (2023-10-05T05:12:14Z)
• Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives [1.7901837062462316]
  We introduce a systematic method to extract the dynamic features from human demonstration to auto-tune the parameters in the Dynamic Movement Primitives framework. Our method was implemented into an actual human-robot setup to extract human dynamic features and used to regenerate the robot trajectories following both LfD and RL.
  arXiv  Detail & Related papers  (2023-04-12T08:48:28Z)
• Ergonomically Intelligent Physical Human-Robot Interaction: Postural Estimation, Assessment, and Optimization [3.681892767755111]
  We show that we can estimate human posture solely from the trajectory of the interacting robot. We propose DULA, a differentiable ergonomics model, and use it in gradient-free postural optimization for physical human-robot interaction tasks.
  arXiv  Detail & Related papers  (2021-08-12T21:13:06Z)
• Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment [1.4502611532302039]
  We show how changing the environment, where the agent locomotes, affects the convergence of morphologies. We show that agent-populations evolving in open-endedly evolving environments exhibit larger morphological diversity than agent-populations evolving in hand crafted curricula of environments.
  arXiv  Detail & Related papers  (2021-04-07T11:28:23Z)
• Bioinspired Bipedal Locomotion Control for Humanoid Robotics Based on EACO [1.0152838128195467]
  This work presents promoting global search capability and convergence rate of the EACO applied to humanoid robots in real-time. We put a special focus on the EACO algorithm on a wide range of problems, from ACO, real-coded GAs, GAs with neural networks(NNs), particle swarm optimization(PSO) to complex robotics systems.
  arXiv  Detail & Related papers  (2020-10-09T09:43:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.