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

Kento Kawaharazuka; Shuhei Ikemoto

REVIEW 3 major objections 5 minor 22 references

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

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A diffusion model can adapt a musculoskeletal robot's body schema to muscle rupture and actuator jamming without retraining.

desk verdict Diffusion-based body schema adaptation is a genuine step over AE/VAE in simulation, but it relies on oracle knowledge of which muscles are broken, so the 'abnormal-state adaptation' claim is more conditional than the abstract suggests. read the letter →

arxiv 2608.01029 v1 pith:MCIXLUWD submitted 2026-08-02 cs.RO

Kento Kawaharazuka , Shuhei Ikemoto This is my paper

classification cs.RO

keywords bodyschemamusculoskeletalrobotdiffusionmodelmuscleruptureactuatorjamminggradient-guideddenoisinglatentspaceredundantactuation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Musculoskeletal robots need a body schema that keeps working when muscles rupture or actuators jam. This paper claims that representing that schema as a diffusion model, operating directly in the high-dimensional space of muscle lengths and tensions, enables adaptation to such abnormal states by gradient-guided denoising, without collecting abnormal-state data or retraining. In a two-joint, six-muscle simulation, the diffusion model satisfies rupture and jamming constraints while keeping static equilibrium and target joint angles, and it does so on all evaluation metrics, unlike autoencoder and variational-autoencoder baselines that each fail on at least one constraint. If correct, a single pretrained generative model could handle multiple concurrent actuator failures on the fly.

What carries the argument

The gradient-guided denoising process: at each denoising step, the model predicts the clean sample $\hat{x}_0$ from the noisy $x_t$ and estimated noise $\hat{\epsilon}_t$; the adaptation loss $L_{\mathrm{adapt}}$ is evaluated on $\hat{x}_0$ and differentiated with respect to $x_t$; then $x_t$ is moved down that gradient before the standard denoising step. The loss encodes the abnormal conditions—zero tension for ruptured muscles, a one-sided length bound for jammed muscles, and nonnegativity plus a maximum on muscle tensions—allowing the diffusion model to enforce physical constraints in the original high-dimensional space without retraining.

What would settle it

Run the same 2-joint, 6-muscle simulation but with incorrect or missing fault indices: for example, hide the true I_cut and I_fix from the loss and instead feed a fault-detection algorithm's estimate (e.g., tension thresholding). If the diffusion adaptation cannot recover a stable posture when the fault set is not perfectly known, the practical claim of adaptation to abnormal states fails.

Watch

Extended reading notes

Core claim

On a simulated two-joint, six-muscle robot, the authors compare three classes of body-schema models—autoencoders, variational autoencoders, and diffusion models—under three abnormal conditions: two ruptured muscles (CUT), two jammed motors (FIX), and one of each (BOTH). They formulate adaptation as a constraint-satisfaction problem: ruptured muscles must have zero tension, jammed muscles must have fixed length, and the result must satisfy static equilibrium and reach the target joint angles. Their diffusion method applies gradient guidance inside the denoising loop: at each step the current sample is partially denoised, scored by a loss that penalizes constraint violations, and the sample is

Load-bearing premise

The adaptation loss requires the indices of the ruptured muscles (I_cut) and jammed motors (I_fix) to be known in advance; the paper never explains how the robot detects which muscles are affected, and if that fault information is wrong or unavailable, the gradient guidance cannot enforce the right constraints.

Editorial extensions

If this is right

With a pretrained diffusion body schema, a musculoskeletal robot can adapt to muscle rupture and actuator jamming in the same adaptation pass, without gathering abnormal-state data or retraining.
The adaptation works for mixed conditions (one rupture plus one jamming), so a single mechanism covers multiple concurrent faults.
The diffusion model outperforms autoencoder and variational-autoencoder baselines on all four evaluation metrics (E_cut, E_fix, E_tau, E_q), regardless of latent-space dimension.
The method requires about 0.7 s per adaptation, which is acceptable for static motions but not for dynamic behavior; the authors note diffusion-acceleration techniques could help.
The same gradient-guided denoising approach is claimed to apply to any robot with redundant sensors and actuators, not just musculoskeletal ones.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper's known-fault-set requirement suggests a natural pairing with an online fault detector: a separate classifier or residual monitor could supply I_cut and I_fix, and the diffusion model would then handle the constraints. The paper does not evaluate this closed loop, but the gradient guidance is compatible with noisy fault estimates.
The diffusion model's consistency on all four metrics hints that its advantage is not representational capacity per se—the parameter counts are nearly equal—but the way gradients act in the full state space. A direct test would be to run the same guidance on a single-step (e.g., distilled) denoiser; if multi-step refinement is what enables adaptation, a one-step model would lose the advantage.
The authors report only exactly two failures per condition; an open question is whether the diffusion advantage persists with three or four failures, where the feasible manifold becomes much smaller and the constraint loss may dominate the denoising signal.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know up front. The diffusion-based body schema with gradient-guided denoising is a real, working idea for adaptation to muscle rupture and actuator jamming in simulation, and the comparison with AE/VAE baselines is more systematic than most. But the method requires exact knowledge of which muscles are cut or jammed — the sets I_cut and I_fix are inputs to the adaptation loss and to the evaluation metrics. That oracle assumption is load-bearing, and the abstract oversells the result by claiming support for partial observations that the experiments never test.

What's genuinely new: formulating body schema adaptation as gradient-guided denoising in the high-dimensional sensor-actuator space, rather than latent-space optimization. The empirical story is clean: across 40 random abnormal cases for CUT, FIX, and BOTH, the diffusion model consistently satisfies rupture and jamming constraints and keeps static equilibrium error and joint angle error low. AE and VAE trade off depending on latent size — small latent spaces hurt representational capacity, large latent spaces hurt adaptation. Diffusion sidesteps that. The box plots are informative, though no significance tests are reported and the evaluation uses the same MuJoCo model used to generate data, which is fine for a simulation study but limits how far you can generalize.

The soft spots, in order of seriousness. First, the oracle issue. The paper never says how the robot knows I_cut and I_fix, and there's no robustness experiment with wrong or missing indices. If a damaged muscle is missed, its constraint is never enforced; if a healthy one is flagged, the guidance actively works against the robot. That can be worse than no adaptation. This isn't a flaw in the denoising formulation, but it's a gap between the claimed 'abnormal-state adaptation' and what's actually demonstrated. Second, the abstract's 'partial observations' claim is not backed by any experiment; the method is evaluated with full observations of q and the abnormal sets. Third, the 0.7 s inference time is acknowledged, and it restricts the method to static motions.

The paper's own limitations paragraph is honest about simulation and speed, but it misses the oracle problem entirely. I'd like to see either a detection mechanism or an explicit conditional framing, plus sensitivity analysis on misreported muscle sets.

Bottom line: this is a solid contribution to a niche but real problem. It deserves a serious referee and likely publication after revision. I'd cite it as related work in any body-schema or failure-adaptation project.

Referee Report

3 major / 5 minor

Summary. The paper proposes a diffusion-model-based body schema for musculoskeletal robots that adapts to muscle rupture and actuator jamming via gradient-guided denoising, without retraining. It compares with autoencoder (AE) and variational autoencoder (VAE) baselines in a simulated two-joint, six-muscle robot. The reported results show the diffusion model achieves low errors across all four metrics (E_cut, E_fix, E_tau, E_q), whereas AE/VAE performance varies with latent dimension. The paper argues that operating directly in high-dimensional sensor-actuator space is more effective than low-dimensional latent optimization for abnormal-state adaptation.

Significance. The idea of using diffusion models for body schema learning is novel in the musculoskeletal robotics context and the paper provides a systematic comparison with AE/VAE baselines under three abnormal conditions. If the approach is validated further, it could be a useful contribution to robot body schema adaptation. The paper's strengths include a clear problem formulation, a reproducible simulation setup, and a consistent evaluation protocol. However, the current claims are limited by the oracle requirement for knowing which muscles are abnormal and by the entirely simulation-based evaluation.

major comments (3)

[Section III.B/C and Section IV.B, Eq. (18)] The adaptation loss L_adapt requires the index sets I_cut and I_fix, i.e., the exact identities of ruptured and jammed muscles. These sets are always supplied in the evaluation ('two randomly selected muscles,' Section IV-B). The paper does not describe how a real robot would obtain this information, nor does it analyze robustness to detection errors. Without this knowledge, the gradient guidance cannot be applied; mis-specified sets could degrade performance. The abstract's claim of 'adaptation to abnormal states' therefore overstates what is demonstrated. Please add a detection mechanism, a robustness analysis with misspecified I_cut/I_fix, or explicitly reframe the claim as 'adaptation given known fault locations.'
[Section V.A and Fig. 6] The paper claims the diffusion model 'consistently achieves stable and strong adaptation performance across all evaluation metrics' and that AE/VAE methods are inferior. However, no statistical significance tests or effect sizes are reported; with 40 samples, some overlaps in the box plots may undermine the qualitative ranking (e.g., for E_q, the text states that VAE-10 and Diff both achieve the best performance). Please report medians/IQRs and perform paired tests (e.g., Wilcoxon signed-rank) across the 40 cases for each metric and condition, or clearly show non-overlapping distributions.
[Section IV.A and IV.B, Eqs. (22)-(29)] The evaluation pipeline reconstructs joint angles q_hat using the same physical model (MuJoCo, Eqs. (1)-(3), static equilibrium) that is used to generate the training data. Thus E_tau and E_q are computed with a model-aware optimizer that may be more favorable than any realizable sensor-based reconstruction. The comparison among methods is fair, but the absolute performance numbers may not transfer to real hardware with model mismatch. The authors acknowledge the simulation-only limitation but not this specific aspect. Please discuss and, if possible, add a model-mismatch experiment (e.g., perturbed spring parameters).

minor comments (5)

[General notation] The notation 'l motor' and 'f f ixed' should use consistent subscripts (e.g., l_motor, f_fixed). The abbreviation 'V AE' is used with a space; use 'VAE' consistently.
[Section II.A] The statement 'none of this information is available during training or during adaptation' is ambiguous because joint angles q are used as input during training and adaptation. Please clarify what model parameters are unknown.
[Eq. (18)] The adaptation loss uses weights C_adapt_fmin and C_adapt_fmax for all muscles. Please add a comment on how these weights were selected and whether the results are sensitive to them.
[Fig. 7] The inference-time comparison shows only the BOTH condition. Consider showing all conditions or stating why BOTH is representative.
[References] Reference [19]: check the spelling of the first author's name (Yoshimitsu vs. Toshimitsu).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the method is an empirical test-time optimization and the metrics are its objectives, not fitted predictions.

full rationale

The paper does not derive a result from a fitted parameter or a self-citation chain. The diffusion model is pretrained on normal-state data (q, l_motor, f), and abnormal-state adaptation is performed at test time by gradient-guided denoising. The adaptation loss in Eq. (18) includes penalties over the ruptured set I_cut and jammed set I_fix, and these same sets appear in the evaluation metrics E_cut and E_fix (Eqs. (33)-(34)). This overlap is the intended objective of the adaptation procedure, not a hidden circularity: the paper is asking whether a pretrained high-dimensional generative model can be guided to satisfy those constraints, and it compares that capability against latent-space optimization. The independent content lies in E_tau and E_q (Eqs. (35)-(36)), which are not directly encoded in L_adapt and still favor the diffusion model. No parameter is fitted to the evaluation data, and no foundational claim rests on a self-citation. The requirement that I_cut and I_fix be known is a real deployment limitation, but it is an input assumption of the adaptation formulation, not a circular reduction. The paper's own limitations section acknowledges the simulation-only scope and future practical issues. Overall, the derivation chain is self-contained and the comparison is fair.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central experiment assumes a specific muscle elasticity model, static equilibrium, and an optimization-based forward reconstruction. The only hand-set parameters are adaptation loss weights and evaluation weights; no new physical entities are introduced. The known-failure-index assumption is listed as a red flag rather than an axiom because it is an operational assumption about the setting, not a physical law.

free parameters (5)

C_adapt_cut = 0.01
Weight for muscle rupture constraint in L_adapt; chosen by hand, not from data.
C_adapt_fix = 1.0
Weight for actuator jamming constraint in L_adapt; hand-tuned.
C_adapt_fmin = 0.01
Weight for nonnegativity constraint on muscle tension; hand-tuned.
C_adapt_fmax = 0.01
Weight for maximum tension constraint; hand-tuned.
C_adapt_lr = 0.01
Learning rate for adaptation gradient; chosen by hand.

assumptions (4)

domain assumption Muscle spring model: fi = k(exp(alpha*delta_i)-1), with k=1000 N, alpha=20 m^-1 (Eq. 1)
Assumes a nonlinear exponential elasticity model for muscles, used to generate training data and evaluation. Not justified from first principles.
domain assumption Static equilibrium is sufficient to determine feasible muscle tensions (optimization in Eq. 22)
Assumes the robot is quasi-static and torques from muscles balance gravity, ignoring dynamics.
domain assumption The optimization-based reconstruction (IRLS) accurately predicts the joint angles that would result from given muscle lengths/tensions
The evaluation uses a numerical optimizer with slack and jamming constraints instead of a physical simulation, assuming this is a faithful forward model.
domain assumption The diffusion model is a good approximation of the conditional distribution p(x|q)
The method assumes the trained diffusion model captures the training data distribution well enough for gradient guidance to work.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots." pith.science (2026). https://pith.science/paper/MCIXLUWD

@misc{pith2026260801029,
  author       = {Pith},
  title        = {Pith review of: Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MCIXLUWD}},
  note         = {Machine review of arXiv:2608.01029}
}

read the original 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.

Figures

Figures reproduced from arXiv: 2608.01029 by the authors.

**Figure 2.** The basic structure of musculoskeletal robots used in this study. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Body schema learning methods based on autoencoders (left), variational autoencoders (center), and diffusion models (right). [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Abnormal states considered in this study: (a) muscle rupture (Cut) [PITH_FULL_IMAGE:figures/full_fig_p003_4.png]

**Figure 5.** Figure 5: Experimental setup using a two-joint, six-muscle musculoskeletal [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 6.** Figure 6: Experimental results of adaptation performance under each abnormal condition. The box-and-whisker plots show the distribution of adaptation [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 7.** Figure 7: Inference time comparison with and without adaptation. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

22 extracted references · 21 canonical work pages

[1]

ECCE1: the first of a series of anthropomimetic musculoskeletal upper torsos,

H. G. Marques, M. J ¨antsh, S. Wittmeier, O. Holland, C. Alessandro, A. Diamond, M. Lungarella, and R. Knight, “ECCE1: the first of a series of anthropomimetic musculoskeletal upper torsos,” in2010 IEEE-RAS International Conference on Humanoid Robots, 2010, pp. 391–396

work page 2010
[2]

Anthrob - A Printed Anthropomimetic Robot,

M. J ¨antsch, S. Wittmeier, K. Dalamagkidis, A. Panos, F. V olkart, and A. Knoll, “Anthrob - A Printed Anthropomimetic Robot,” in2013 IEEE-RAS International Conference on Humanoid Robots, 2013, pp. 342–347

work page 2013
[3]

Human Mimetic Musculoskele- tal Humanoid Kengoro toward Real World Physically Interactive Actions,

Y . Asano, T. Kozuki, S. Ookubo, M. Kawamura, S. Nakashima, T. Katayama, Y . Iori, H. Toshinori, K. Kawaharazuka, S. Makino, Y . Kakiuchi, K. Okada, and M. Inaba, “Human Mimetic Musculoskele- tal Humanoid Kengoro toward Real World Physically Interactive Actions,” in2016 IEEE-RAS International Conference on Humanoid Robots, 2016, pp. 876–883

work page 2016
[4]

Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems,

K. Kawaharazuka, S. Makino, K. Tsuzuki, M. Onitsuka, Y . Nagamatsu, K. Shinjo, T. Makabe, Y . Asano, K. Okada, K. Kawasaki, and M. In- aba, “Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems,” in2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019, pp. 7294–7301

work page 2019
[5]

Characteristics, Man- agement, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi,

K. Kawaharazuka, K. Okada, and M. Inaba, “Characteristics, Man- agement, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi,”Advanced Intelligent Systems, 2026

work page 2026
[6]

Control strategies for tendon-driven manipulators,

S. C. Jacobsen, H. Ko, E. K. Iversen, and C. C. Davis, “Control strategies for tendon-driven manipulators,”IEEE Control Systems Magazine, vol. 10, no. 2, pp. 23–28, 1990

work page 1990
[7]

Mechanical stiffness control of tendon-driven joints,

K. Koganezawa and M. Yamazaki, “Mechanical stiffness control of tendon-driven joints,” in1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1999, pp. 818–825

work page 1999
[8]

Reflex-like control for the coupled tendon- driven manipulator,

H. Endo and M. Wada, “Reflex-like control for the coupled tendon- driven manipulator,” in1994 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, 1994, pp. 1810–1815

work page 1994

Show all 22 references

[9]

Self-organization of reflexive behavior from spontaneous motor activity,

H. G. Marques, F. Imtiaz, F. Iida, and R. Pfeifer, “Self-organization of reflexive behavior from spontaneous motor activity,”Biological Cybernetics, vol. 107, no. 1, pp. 25–37, 2013

2013
[10]

Robotic investigation on effect of stretch reflex and crossed inhibitory response on bipedal hopping,

X. Liu, A. Rosendo, S. Ikemoto, M. Shimizu, and K. Hosoda, “Robotic investigation on effect of stretch reflex and crossed inhibitory response on bipedal hopping,”Journal of The Royal Society Interface, vol. 15, no. 140, p. 20180024, 2018

2018
[11]

Robust Continuous Motion Strategy Against Muscle Rupture using Online Learning of Redundant Intersensory Networks for Musculoskeletal Humanoids,

K. Kawaharazuka, M. Nishiura, Y . Toshimitsu, Y . Omura, Y . Koga, Y . Asano, K. Okada, K. Kawasaki, and M. Inaba, “Robust Continuous Motion Strategy Against Muscle Rupture using Online Learning of Redundant Intersensory Networks for Musculoskeletal Humanoids,” Robotics and Au...

2022
[12]

Body Image and Body Schema: A Conceptual Clarifi- cation,

S. Gallagher, “Body Image and Body Schema: A Conceptual Clarifi- cation,”The Journal of Mind and Behavior, vol. 7, no. 4, pp. 541–554, 1986

1986
[13]

Disorders of Body Scheme,

P. Haggard and D. M. Wolpert, “Disorders of Body Scheme,” in Higher-Order Motor Disorders, Ed. Freund, Jeannerod, Hallett, and Leiguarda. Oxford University Press, 2005

2005
[14]

Joint propriocep- tion acquisition strategy based on joints-muscles topological maps for musculoskeletal humanoids,

Y . Nakanishi, K. Hongo, I. Mizuuchi, and M. Inaba, “Joint propriocep- tion acquisition strategy based on joints-muscles topological maps for musculoskeletal humanoids,” in2010 IEEE International Conference on Robotics and Automation, 2010, pp. 1727–1732

2010
[15]

Learning Nonlinear Muscle-Joint State Mapping Toward Geometric Model-Free Tendon Driven Musculoskeletal Robots,

S. Ookubo, Y . Asano, T. Kozuki, T. Shirai, K. Okada, and M. Inaba, “Learning Nonlinear Muscle-Joint State Mapping Toward Geometric Model-Free Tendon Driven Musculoskeletal Robots,” in2015 IEEE- RAS International Conference on Humanoid Robots, 2015, pp. 765– 770

2015
[16]

Online Learning of Joint-Muscle Mapping using Vision in Tendon-driven Musculoskeletal Humanoids,

K. Kawaharazuka, S. Makino, M. Kawamura, Y . Asano, K. Okada, and M. Inaba, “Online Learning of Joint-Muscle Mapping using Vision in Tendon-driven Musculoskeletal Humanoids,”IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 772–779, 2018

2018
[17]

Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures,

K. Kawaharazuka, K. Tsuzuki, S. Makino, M. Onitsuka, Y . Asano, K. Okada, K. Kawasaki, and M. Inaba, “Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures,”IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2965–2972, 2019

2019
[18]

Musculoskeletal AutoEncoder: A Uni- fied Online Acquisition Method of Intersensory Networks for State Estimation, Control, and Simulation of Musculoskeletal Humanoids,

K. Kawaharazuka, K. Tsuzuki, M. Onitsuka, Y . Asano, K. Okada, K. Kawasaki, and M. Inaba, “Musculoskeletal AutoEncoder: A Uni- fied Online Acquisition Method of Intersensory Networks for State Estimation, Control, and Simulation of Musculoskeletal Humanoids,” IEEE Robotics and...

2020
[19]

Forward/Inverse Kinematics Modeling for Tensegrity Manipulator Based on Goal-Conditioned Variational Autoencoder,

Y . Yoshimitsu, T. Osa, and S. Ikemoto, “Forward/Inverse Kinematics Modeling for Tensegrity Manipulator Based on Goal-Conditioned Variational Autoencoder,” in2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023, pp. 6668–6673

2023
[20]

Self-organization of behavioral primitives as multiple attractor dynamics: a robot experiment,

J. Tani, “Self-organization of behavioral primitives as multiple attractor dynamics: a robot experiment,” in2002 International Joint Conference on Neural Networks, 2002, pp. 489–494

2002
[21]

Robust regression using iteratively reweighted least-squares,

P. W. Holland and R. E. Welsch, “Robust regression using iteratively reweighted least-squares,”Communications in Statistics – Theory and Methods, vol. 6, no. 9, pp. 813–827, 1977

1977
[22]

Automatic Grouping of Redundant Sensors and Actuators Using Functional and Spatial Connections: Application to Muscle Grouping for Musculoskeletal Humanoids,

K. Kawaharazuka, M. Nishiura, Y . Koga, Y . Omura, Y . Toshimitsu, Y . Asano, K. Okada, K. Kawasaki, and M. Inaba, “Automatic Grouping of Redundant Sensors and Actuators Using Functional and Spatial Connections: Application to Muscle Grouping for Musculoskeletal Humanoids,”IEE...

1981

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

ECCE1: the first of a series of anthropomimetic musculoskeletal upper torsos,

H. G. Marques, M. J ¨antsh, S. Wittmeier, O. Holland, C. Alessandro, A. Diamond, M. Lungarella, and R. Knight, “ECCE1: the first of a series of anthropomimetic musculoskeletal upper torsos,” in2010 IEEE-RAS International Conference on Humanoid Robots, 2010, pp. 391–396

work page 2010

[2] [2]

Anthrob - A Printed Anthropomimetic Robot,

M. J ¨antsch, S. Wittmeier, K. Dalamagkidis, A. Panos, F. V olkart, and A. Knoll, “Anthrob - A Printed Anthropomimetic Robot,” in2013 IEEE-RAS International Conference on Humanoid Robots, 2013, pp. 342–347

work page 2013

[3] [3]

Human Mimetic Musculoskele- tal Humanoid Kengoro toward Real World Physically Interactive Actions,

Y . Asano, T. Kozuki, S. Ookubo, M. Kawamura, S. Nakashima, T. Katayama, Y . Iori, H. Toshinori, K. Kawaharazuka, S. Makino, Y . Kakiuchi, K. Okada, and M. Inaba, “Human Mimetic Musculoskele- tal Humanoid Kengoro toward Real World Physically Interactive Actions,” in2016 IEEE-RAS International Conference on Humanoid Robots, 2016, pp. 876–883

work page 2016

[4] [4]

Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems,

K. Kawaharazuka, S. Makino, K. Tsuzuki, M. Onitsuka, Y . Nagamatsu, K. Shinjo, T. Makabe, Y . Asano, K. Okada, K. Kawasaki, and M. In- aba, “Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems,” in2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019, pp. 7294–7301

work page 2019

[5] [5]

Characteristics, Man- agement, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi,

K. Kawaharazuka, K. Okada, and M. Inaba, “Characteristics, Man- agement, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi,”Advanced Intelligent Systems, 2026

work page 2026

[6] [6]

Control strategies for tendon-driven manipulators,

S. C. Jacobsen, H. Ko, E. K. Iversen, and C. C. Davis, “Control strategies for tendon-driven manipulators,”IEEE Control Systems Magazine, vol. 10, no. 2, pp. 23–28, 1990

work page 1990

[7] [7]

Mechanical stiffness control of tendon-driven joints,

K. Koganezawa and M. Yamazaki, “Mechanical stiffness control of tendon-driven joints,” in1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1999, pp. 818–825

work page 1999

[8] [8]

Reflex-like control for the coupled tendon- driven manipulator,

H. Endo and M. Wada, “Reflex-like control for the coupled tendon- driven manipulator,” in1994 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, 1994, pp. 1810–1815

work page 1994