Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots

• URL: http://arxiv.org/abs/2608.01029v1
• Date: Sun, 02 Aug 2026 06:10:03 GMT
• Title: Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots
• Abstract summary: Musculoskeletal robots require an internal body schema that remains consistent under a wide range of physical state changes.<n>This study proposes a diffusion-based framework for body schema learning in musculoskeletal robots.
• Score: 5.053821868443824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Musculoskeletal robots require an internal body schema that remains consistent under a wide range of physical state changes, including abnormalities such as muscle rupture and actuator jamming. Conventional approaches based on autoencoders or variational autoencoders learn average behaviors by projecting sensor and actuator signals into a low-dimensional latent space; however, exploration within the latent space alone has limited capability to handle out-of-distribution or abnormal states that are not included in the training data. To address this limitation, this study proposes a diffusion-based framework for body schema learning in musculoskeletal robots. Unlike generative models that operate through low-dimensional latent spaces, diffusion models can directly and iteratively estimate physically consistent sensor and actuator values in the high-dimensional space through a denoising process, even under partial observations and constraints, without requiring retraining. By formulating body schema adaptation as a gradient-guided denoising process, the proposed method enables adaptive estimation of appropriate muscle lengths and muscle tensions even under abnormal conditions such as muscle rupture and actuator jamming. The validity of the proposed framework is verified through simulation experiments using a musculoskeletal robot model.

Related papers

• Diffusion-Based Generation of Gait Trajectories [1.1873304786619876]
  Generation of musculoskeletal gait trajectories conditioned on patient-specific parameters is a key challenge for wearable robotics and rehabilitation.<n>In this work, we explore conditional diffusion models for generating lower-limb joint-angle trajectories conditioned on gait parameters.<n>Experiments on a dataset of 4,590 gait cycles show that diffusion models can generate realistic periodic gait trajectories.
  arXiv  Detail & Related papers  (2026-09-13T16:27:52Z)
• From Brain Models to Executable Digital Twins: Execution Semantics and Neuro-Neuromorphic Systems [51.82266520875928]
  This survey introduces physically constrained executability as a unifying perspective for comparing approaches at the level of execution.<n>I propose a taxonomy of execution regimes ranging from isolated offline models to coordinated co-simulation, to continuously executing digital twins sustained by online data.<n>The executability concept clarifies why accuracy alone is insufficient, and motivates an agenda centered on semantic interoperability, hybrid-time correctness, evaluation protocols, scalable assimilation, and safe closed-loop validation.
  arXiv  Detail & Related papers  (2026-04-15T07:34:46Z)
• Musculoskeletal Motion Imitation for Learning Personalized Exoskeleton Control Policy in Impaired Gait [1.224954637705144]
  We introduce a device-agnostic framework to enable scalable personalized exoskeleton assistance for both able-bodied and clinical populations.<n>Our control policies generate physiologically plausible locomotion dynamics and capture clinically observed compensatory strategies under targeted muscular deficits.<n>Without task-specific tuning, the resulting exoskeleton control policies produce assistive torque profiles at the hip and ankle that align with state-of-the-art profiles validated in human experiments.
  arXiv  Detail & Related papers  (2026-04-10T15:49:24Z)
• Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale [38.15316304510102]
  MuscleMimic is an open-source framework for motion imitation learning with physiologically realistic, muscle-driven models.<n>By lowering the computational and data barriers to musculoskeletal simulation, MuscleMimic enables systematic model validation across diverse dynamic movements.
  arXiv  Detail & Related papers  (2026-03-26T15:18:37Z)
• AdaMorph: Unified Motion Retargeting via Embodiment-Aware Adaptive Transformers [49.796014934503184]
  We present a unified neural framework that enables a single model to adapt human motion to diverse robot morphologies.<n> Experimental results on 12 distinct humanoid robots demonstrate that AdaMorph effectively control across heterogeneous topologies.
  arXiv  Detail & Related papers  (2026-01-12T07:39:38Z)
• Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data [6.770726279203919]
  Hydraulically electrostatic (HASEL) actuators offer high performance but exhibit memory effects and musculoskeletal effects.<n>We present a data-driven reduction and control strategy grounded in spectral submanifold (SSM) theory.<n>We deploy the SSM-based model for real-time control of an antagonistic HASEL-clutch joint.
  arXiv  Detail & Related papers  (2026-01-06T18:43:49Z)
• Allostatic Control of Persistent States in Spiking Neural Networks for perception and computation [79.16635054977068]
  We introduce a novel model for updating perceptual beliefs about the environment by extending the concept of Allostasis to the control of internal representations.<n>In this paper, we focus on an application in numerical cognition, where a bump of activity in an attractor network is used as a spatial numerical representation.
  arXiv  Detail & Related papers  (2025-03-20T12:28:08Z)
• A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-skeletal Control [1.053652600598537]
  We investigate how Latent Diffusion Models can custom synthesize coronary anatomy for virtual intervention studies. Our framework enables the generation and editing of coronary anatomy in a controllable manner, allowing device designers to derive mechanistic insights.
  arXiv  Detail & Related papers  (2024-07-22T13:44:06Z)
• Morphological Symmetries in Robotics [45.32599550966704]
  morphological symmetries are intrinsic properties of the robot's morphology.<n>These symmetries extend to the robot's state space and sensor measurements.<n>For data-driven methods, we demonstrate that morphological symmetries can enhance the sample efficiency and generalization of machine learning models.<n>In the context of analytical methods, we employ abstract harmonic analysis to decompose the robot's dynamics into a superposition of lower-dimensional, independent dynamics.
  arXiv  Detail & Related papers  (2024-02-23T17:21:21Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)

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.