Pith. sign in

REVIEW 4 major objections 6 minor 18 references

Deep-Learning Control of Lower-Limb Exoskeletons via simplified Therapist Input

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read A three-step data-driven controller can replace the hierarchical control of partial-assistance exoskeletons, inferring gait features from sensor history, letting a therapist adjust them live, and using prediction uncertainty to render…

desk verdict Clean feasibility study with a genuinely new three-step pipeline; watch out for the unbounded impedance law in Eqs. 2-3 when you referee it. read the letter →

arxiv 2412.07959 v2 pith:FYCLNV2X submitted 2024-12-10 cs.RO cs.LG

Lorenzo Vianello , Clément Lhoste , Emek Bar{i}ş Küçüktabak , Matthew Short , Levi Hargrove , Jose L. Pons This is my paper · ORCID
classification cs.ROcs.LG
keywords lower-limbexoskeletonpartialassistancedeeplearningcontrolgaitfeaturesimpedancetherapistinterfacestairwalkinguncertainty
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

This paper tries to show that the hierarchical controllers used in partial-assistance exoskeletons, which demand extensive per-user and per-activity calibration, can be replaced by a three-step data-driven controller that works without user-specific tuning. The controller infers a handful of clinically meaningful gait features from a short window of sensor data, lets a therapist adjust those features at runtime through a simple interface, and then predicts the desired joint posture, scaling the impedance of the spring-damper interaction with prediction uncertainty. Validated on two healthy users walking on a treadmill and ascending and descending stairs, the approach yields negative interaction power at the hip and knee (mean −2.1 ± 1.6 W and −0.6 ± 1.4 W), which the authors read as the exoskeleton assisting the user. A sympathetic reader would take the paper's contribution to be a proof of concept that therapist-friendly feature adjustment and data-driven intent inference can be fused into one calibration-free control loop.

What carries the argument

The load-bearing mechanism is the two-stage probabilistic regression chain, with each stage an ensemble of six independently trained neural networks whose outputs are fit to a normal distribution. The Features Extractor Model (a bidirectional LSTM, a recurrent network that reads a 300 ms window of sensor data) yields self-selected gait features; the Command Predictor Model (a dense feedforward network) maps the operator-adjusted feature vector to desired joint kinematics. Uncertainty propagates through Monte-Carlo sampling, and the inverse of the predicted variance scales the impedance gains, so the same network that generates the reference also generates the compliance.

What would settle it

Ask a new user to walk with an operator-selected step length larger than any in the training set and record the knee reference during swing; if the model outputs a non-physiological trajectory (hyperextension, or foot clearance below ground) or the hip or knee interaction power turns positive for that out-of-distribution setting, the central claim of calibration-free assistance would fail.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that a two-stage neural pipeline can replace the high-, mid-, and low-level control hierarchy of a partial-assistance exoskeleton. In the first stage, an ensemble of bidirectional LSTM networks (a type of recurrent neural network) maps the last 300 ms of joint angles, velocities, trunk orientation, interaction torques, and foot-plate forces to a probability distribution over landing step length, landing step height, step clearance, step velocity, and gait phase. In the second stage, after a therapist may modify those features—including a phase increment $\delta$ that anticipates the user's movement—an ensemble of dense networks regresses the desired hip and knee joint positions and velocities, again as a distribution. The uncertainty of that prediction sets the stiffness and damping of the torque law $\tau^* = K(\theta - \theta_d) + D(\dot{\theta} - \dot{\theta}_d)$ via $K = K_S(1 - \sigma_\theta/\sigma_{\max})$ and $D = D_S(1 - \sigma_{\dot{\theta}}/\sigma_{\max})$, making the robot more compliant when the forecast is uncertain. The evidence offered is that operator-selected changes in step clearance, speed, and length produce the expected kinematic changes, and that interaction power is predominantly negative across treadmill and stair conditions.

Load-bearing premise

The nine healthy participants' data, collected under a transparency controller and a state-machine controller, must be representative enough that the learned mapping from operator-adjusted features to joint kinematics stays safe and physiological for a new user, even for feature values the training data never showed.

Editorial extensions

If this is right

  • Partial-assistance exoskeleton control could drop the activity-specific finite-state machines and torque profile libraries, since the controller learns the mapping from sensor history to features and from features to kinematics from data.
  • The same interface gives a therapist an interpretable handle on gait: increasing step clearance raises joint range of motion, increasing step speed raises joint velocities, and increasing step length raises hip range of motion in the observed data.
  • Because stiffness and damping fall as prediction uncertainty rises, the device automatically becomes softer when intent is ambiguous, which the authors describe as an added safety property.
  • The reported negative interaction power at hip and knee during swing is the authors' evidence that the predicted references yield net assistance rather than resistance across treadmill and stair conditions.

Reading between the lines

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

  • Beyond the paper, the uncertainty-scaled impedance law could serve as a built-in safety envelope for clinical deployment: if a therapist requests a step length far outside the training distribution, the Command Predictor Model's predictive variance would rise and the robot would soften automatically before a hard constraint is needed.
  • The R² = 0.68 for joint kinematics suggests the five-feature representation leaves a large share of walking variability unexplained; adding features such as step width, trunk tilt, or a learned latent state might raise prediction fidelity without needing per-user calibration, a testable extension.
  • The two-user validation is an honest first test of the no-calibration claim; a stronger test would run the same pipeline on patients with mild gait impairments, where the healthy training distribution may not cover pathological patterns, and check whether interaction power stays negative.
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.

Referee Report

4 major / 6 minor

Summary. The manuscript proposes replacing the hierarchical controller of a partial-assistance lower-limb exoskeleton with a three-step data-driven pipeline: a Features Extractor Model (FEM) maps 300 ms windows of sensor data to probabilistic estimates of clinically relevant gait features (step length, step height, step velocity, step clearance, and gait phase); an operator/therapist can modify these features through a user interface; and a Command Predictor Model (CPM) maps the features to reference hip/knee joint kinematics, with impedance stiffness and damping scaled inversely to prediction uncertainty (Eqs. 2-3). The models are trained offline on data from nine healthy participants walking under haptic-transparency and state-machine controllers, with interpolation-based data augmentation. Online validation with two healthy participants during treadmill walking and stair ascent/descent shows that operator-modified features change the reference kinematics and that hip and knee interaction power are predominantly negative, which the authors interpret as exoskeleton assistance.

Significance. The idea of simplifying exoskeleton control by letting therapists adjust clinically interpretable gait features, while using probabilistic deep learning to generate reference kinematics and impedance, is timely and potentially valuable for rehabilitation practice. The paper's strengths include a working real-time implementation on a commercial exoskeleton, explicit uncertainty propagation through the pipeline, and a user interface that addresses a real clinical need. However, the evidence as presented is preliminary: only two healthy participants were tested online, no statistical tests or baseline comparisons are reported, and the impedance rule in Eqs. 2-3 has a safety-relevant gap in the high-uncertainty regime. The contribution is promising but not yet convincingly established.

major comments (4)
  1. [§II-D.a, Eqs. (2)-(3)] The stiffness and damping are defined as K = KS(1 - σθ/σmax) and D = DS(1 - σdot/σmax), and the manuscript states no lower bound or clipping for K and D. Because the CPM is a regression network without explicit out-of-distribution handling, an operator-induced feature change (e.g., increased step clearance or step length beyond the training/augmented range, as invited by the interface in Fig. 1.B) can produce predictive standard deviations above the validation-set maximum σmax. In that regime both K and D become negative, so the impedance controller would push the user away from the reference and inject energy, contradicting the conclusion that adaptive compliance enhances safety. Please specify the implemented safeguard (e.g., clipping the uncertainty ratios, clamping K and D at zero, or rejecting out-of-distribution features) or, if no safeguard exists, quantify the range of σ observed online and explain why σ can never exceed σmax.
  2. [§II-D.b and §III] The central assistance claim rests on only two healthy participants, each performing a single 15-minute treadmill session and five stair repetitions, with no statistical tests and no comparison against a baseline controller such as haptic transparency or the state-machine controller. Reporting only aggregate mean interaction power (-2.1 ± 1.6 W for the hip and -0.6 ± 1.4 W for the knee) and two positive power peaks (around 15% and 40% of the gait cycle, Fig. 5) without per-condition statistics makes it impossible to distinguish genuine assistance from measurement variability or transient adaptation. Additional participants, repeated sessions, and a baseline comparison (or at minimum a per-condition breakdown with confidence intervals) are needed before the assistance claim can be supported.
  3. [§II-B and §II-C.b] The CPM is trained on data collected under haptic transparency and a state-machine controller, while the online evaluation closes the loop with self-selected features (Fig. 2.B-F); the training distribution may therefore not match the closed-loop data distribution. The weighted-interpolation augmentation assumes that interpolating between measured activities preserves valid biomechanical relationships, but this assumption is not tested: no analysis reports whether the online feature values fall within the training/augmented support, and no out-of-distribution detection is provided. Without such an analysis, the risk that the CPM extrapolates to non-physiological reference trajectories for new users remains open.
  4. [§III, discussion of Fig. 2] Offline CPM accuracy is moderate (R²=0.68), and joint positions and velocities are predicted by independent models, so the resulting stiffness and damping values are not guaranteed to be physically consistent; the authors themselves attribute the observed under-damped early-stance behavior to this independence. Because the impedance parameters are the safety-relevant output of the pipeline, the manuscript should either model K and D jointly (or with coupled uncertainty) or demonstrate that the independent predictions do not produce unstable interaction dynamics beyond the single observed under-damping effect.
minor comments (6)
  1. [Abstract and §III] The abstract and the results section give slightly different mean hip interaction power (-2.1 W vs -2.05 W); please make the reported values consistent.
  2. [Fig. 3 caption] The caption refers to 'two users in the training dataset' although the text says these are two of the nine users used to develop the model (seven for training, two for validation); please clarify the role of these users.
  3. [Fig. 1] The architecture diagram lists dropout rates as '0.15%' in some places and '0.15' in others; please state whether these are percentages or fractions and make the notation uniform.
  4. [§II-C] The hyperparameter selection is described only as 'heuristic'; for reproducibility, report the searched ranges and the chosen values for the FEM and CPM architectures, including the number of Monte-Carlo samples N used in Sec. II-C.c.
  5. [Eqs. (2)-(3)] The conditions 'with σθ/σmax > 0' and 'with σdot/σmax > 0' are redundant because standard deviations are nonnegative; the intended domain is presumably 0 ≤ σ/σmax ≤ 1. Please state the intended constraint explicitly.
  6. [§III, R² value] The reported R²=0.68 is not accompanied by confidence intervals or a per-feature/per-joint breakdown; please clarify which regression this value refers to and how variable it is across the independent models.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity; the assistance metric is measured independently of the fitted mappings.

full rationale

The derivation chain is: FEM maps recent sensor windows to gait features; the therapist/operator may adjust these features; CPM maps the features to desired joint kinematics and velocities; uncertainty from the ensemble scales the impedance gains (Eqs. 2-3); and assistance is assessed by measured interaction power (τ_int·θdot). The interaction power reported in Fig. 5 is not a training target or a fitted output, so the negative-power result is not forced by construction. The CPM is trained on kinematics collected under two existing controller conditions (Sec. II-B), but that is data provenance rather than a parameter fitted to the target outcome, and validation uses two external users. Eq. 2-3 is a stated design rule with K_S and D_S taken from prior impedance literature, and the uncertainty σ comes from the ensemble of independently trained networks; no equation reduces the predicted assistance to a fitted parameter. Several overlapping-author citations appear ([12] for the exoskeleton hardware and impedance plant, [13] for the CORC software stack, [14] for gait-phase encoding, [16] for the Monte-Carlo uncertainty propagation), but none is used as a uniqueness theorem or as the sole justification for the controller's behavior; they are implementation details with independent published content. The concern about possible negative stiffness/damping when σ exceeds σ_max in Eqs. 2-3 is a correctness and safety issue, not a circularity. Thus the central claim remains self-contained against the measured interaction power, and the low score reflects only minor, non-load-bearing self-citations.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claim depends on design choices rather than new physical laws. The dominant free parameters are the impedance scales K_S and D_S picked from prior work, the uncertainty normalizer sigma_max, the hand-selected gait feature set, and the model hyperparameters. The main axioms are domain assumptions about feature sufficiency, uncertainty interpretation, and the representativeness of training data. No new physical entities are introduced.

free parameters (6)
  • Stiffness scale K_S = [80, 60] Nm/rad for hip and knee
    Selected from prior work [9], [18]; sets the magnitude of assistive torque in Eq. (2) and is central to the claim of assistance.
  • Damping scale D_S = [8, 6] Nm·s/rad for hip and knee
    Selected from prior work; sets damping in Eq. (3).
  • sigma_max = Maximal uncertainty across the validation set
    Normalizes stiffness and damping in Eqs. (2)-(3); the choice affects how often the controller is stiff versus compliant.
  • Gait feature set = step height, step length, step velocity, step clearance, gait phase
    The hand-selected set of clinically relevant features is a modeling assumption; the paper admits it may not capture full gait complexity.
  • Model hyperparameters = noise std 0.01, dropout 0.15, kernel reg 0.01, 6 ensemble networks, 300 ms window, 100 epochs
    Selected heuristically in Sec. II-C; they influence prediction uncertainty and therefore the impedance rendered by the controller.
  • Gait-phase anticipation delta = Manual increment added to the phase mean
    Operator-adjustable parameter that shifts the timing of assistance; described as a-dimensional in Sec. II-D.1.
assumptions (6)
  • domain assumption The five selected gait features are sufficient to represent locomotion intent for exoskeleton control.
    Sec. II-C.1 and Sec. III state this as a limitation; if false, the command predictor cannot command appropriate kinematics.
  • domain assumption Negative interaction power τ_int·dot_θ indicates assistive torque.
    Fig. 5 caption defines negative joint power as assistance; this interpretation underlies the main validation claim.
  • domain assumption Strain-gauge based τ_int accurately measures user-exoskeleton interaction torque.
    Relied on for the interaction power metric; hardware is taken from [12] without independent validation in this paper.
  • domain assumption Ensemble spread across six networks approximates predictive uncertainty.
    Probabilistic estimation in Sec. II-C.3; the spread is used to set K and D in Eqs. (2)-(3).
  • domain assumption Training data from transparency and state-machine controllers are representative of normative gait for new users.
    Sec. II-B; if biased, modified features may map to unsafe or non-physiological kinematics.
  • ad hoc to paper Data augmentation by interpolating features and kinematics preserves valid biomechanical relationships.
    Sec. II-C.2 creates synthetic training pairs that were not measured experimentally.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Deep-Learning Control of Lower-Limb Exoskeletons via simplified Therapist Input." pith.science (2026). https://pith.science/paper/FYCLNV2X

@misc{pith2026241207959,
  author       = {Pith},
  title        = {Pith review of: Deep-Learning Control of Lower-Limb Exoskeletons via simplified Therapist Input},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FYCLNV2X}},
  note         = {Machine review of arXiv:2412.07959}
}
read the original abstract

Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of normative walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require extensive calibration due to the complexity of the controller and user-specific parameter tuning, especially for activities like stair or ramp navigation. To address the limitations of hierarchical control in exoskeletons, this work proposes a three-step, data-driven approach: (1) using recent sensor data to probabilistically infer locomotion states (landing step length, landing step height, walking velocity, step clearance, gait phase), (2) allowing therapists to modify these features via a user interface, and (3) using the adjusted locomotion features to predict the desired joint posture and model stiffness in a spring-damper system based on prediction uncertainty. We evaluated the proposed approach with two healthy participants engaging in treadmill walking and stair ascent and descent at varying speeds, with and without external modification of the gait features through a user interface. Results showed a variation in kinematics according to the gait characteristics and a negative interaction power suggesting exoskeleton assistance across the different conditions.

Figures

Figures reproduced from arXiv: 2412.07959 by the authors.

Figure 1
Figure 1. (1) Data-driven controller:(1.A) Sensor readings are passed to multiple deep-learning models to estimate representative features of the walking pattern. Thes resulting features are fitted to a normal distribution to consider the uncertanty of the locomotion pattern; (1.B) User interface allows to modify the self-selected locomotion features to allow the therapist to adapt the locomotion depending on the needs of eac… view at source ↗
Figure 2
Figure 2. Example of walking pattern for a single subject. In order, the five rows show: the implemented features (self-selected or operator-selected) over [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Example of gait feature prediction. The plot displays the ground [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Joint kinematic across gait features (both self-selected and operator-selected): The two rows display respectively the hip and knee joint angles (left [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Joint power across gait features (both self-selected and operator-selected): The two rows display the hip and knee joint power (left and right [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

  1. [1]

    Review of control strategies for lower-limb exoskeletons to assist gait,

    R. Baud, A. R. Manzoori, A. Ijspeert, and M. Bouri, “Review of control strategies for lower-limb exoskeletons to assist gait,”Journal of NeuroEngineering and Rehabilitation , vol. 18, no. 1, pp. 1–34, 2021

    work page 2021

  2. [2]

    Twiice—a lightweight lower-limb exoskeleton for complete paraplegics,

    T. V ouga, R. Baud, J. Fasola, M. Bouri, and H. Bleuler, “Twiice—a lightweight lower-limb exoskeleton for complete paraplegics,” in 2017 International Conference on Rehabilitation Robotics (ICORR) . IEEE, 2017, pp. 1639–1645

    work page 2017

  3. [3]

    Human-robot interaction tests on a novel robot for gait assistance,

    N. L. Tagliamonte, F. Sergi, G. Carpino, D. Accoto, and E. Guglielmelli, “Human-robot interaction tests on a novel robot for gait assistance,” in 2013 IEEE 13th international conference on rehabilitation robotics (ICORR) . IEEE, 2013, pp. 1–6

    work page 2013

  4. [4]

    Control strategies used in lower limb ex- oskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness,

    J. de Miguel-Fern ´andez, J. Lobo-Prat, E. Prinsen, J. M. Font-Llagunes, and L. Marchal-Crespo, “Control strategies used in lower limb ex- oskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness,” Journal of NeuroEngineering and Rehabilitation , vol. 20, no. 1, Feb. 2023

    work page 2023

  5. [5]

    Haptic training: Which types facilitate (re)learning of which motor task and for whom? answers by a review,

    E. Basalp, P. Wolf, and L. Marchal-Crespo, “Haptic training: Which types facilitate (re)learning of which motor task and for whom? answers by a review,” IEEE Transactions on Haptics , vol. 14, no. 4, pp. 722–739, 2021

    work page 2021

  6. [6]

    Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees,

    M. Kim, A. M. Simon, and L. J. Hargrove, “Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees,” Wearable Technologies, vol. 3, 2022

    work page 2022

  7. [7]

    Deep-learning estimation of weight distribution using joint kinematics for lower-limb exoskeleton control,

    C. Lhoste, E. B. K ¨uc ¸¨uktabak, L. Vianello, L. Amato, M. R. Short, K. M. Lynch, and J. L. Pons, “Deep-learning estimation of weight distribution using joint kinematics for lower-limb exoskeleton control,” IEEE Transactions on Medical Robotics and Bionics , pp. 1–1, 2024

    work page 2024

  8. [8]

    Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes,

    A. M. Simon, K. A. Ingraham, N. P. Fey, S. B. Finucane, R. D. Lipschutz, A. J. Young, and L. J. Hargrove, “Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes,” PloS one , vol. 9, no. 6, p. e99387, 2014

    work page 2014

Show all 18 references
  1. [9]

    Modeling and stiffness- based continuous torque control of lightweight quasi-direct-drive knee exoskeletons for versatile walking assistance,

    T.-H. Huang, S. Zhang, S. Yu, M. K. MacLean, J. Zhu, A. Di Lallo, C. Jiao, T. C. Bulea, M. Zheng, and H. Su, “Modeling and stiffness- based continuous torque control of lightweight quasi-direct-drive knee exoskeletons for versatile walking assistance,” IEEE Transactions on Rob...

    2022

  2. [10]

    Data-driven variable impedance control of a powered knee–ankle prosthesis for adaptive speed and incline walking,

    T. K. Best, C. G. Welker, E. J. Rouse, and R. D. Gregg, “Data-driven variable impedance control of a powered knee–ankle prosthesis for adaptive speed and incline walking,” IEEE Transactions on Robotics , 2023

    2023

  3. [11]

    Task-agnostic exoskeleton control via biological joint moment estimation,

    D. D. Molinaro, K. L. Scherpereel, E. B. Schonhaut, G. Evangelopou- los, M. K. Shepherd, and A. J. Young, “Task-agnostic exoskeleton control via biological joint moment estimation,” Nature, vol. 635, no. 8038, pp. 337–344, 2024

    2024

  4. [12]

    Haptic transparency and interaction force control for a lower limb exoskele- ton,

    E. B. K ¨uc ¸¨uktabak, Y . Wen, S. J. Kim, M. R. Short, D. Ludvig, L. Hargrove, E. J. Perreault, K. M. Lynch, and J. L. Pons, “Haptic transparency and interaction force control for a lower limb exoskele- ton,” IEEE Transactions on Robotics , vol. 40, pp. 1842–1859, 2024

    2024

  5. [13]

    Canopen robot controller (corc): An open software stack for human robot interaction development,

    J. Fong, E. B. K ¨uc ¸¨uktabak, V . Crocher, Y . Tan, K. M. Lynch, J. L. Pons, and D. Oetomo, “Canopen robot controller (corc): An open software stack for human robot interaction development,” inWearable Robotics: Challenges and Trends . Cham: Springer International Publishing...

    2022

  6. [14]

    A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs,

    M. Kim and L. J. Hargrove, “A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs,” Frontiers in Neurorobotics, vol. 16, p. 1064313, 2023

    2023

  7. [15]

    Machine learning approaches for ac- tivity recognition and/or activity prediction in locomotion assistive devices—a systematic review,

    F. Labarri `ere, E. Thomas, L. Calistri, V . Optasanu, M. Gueugnon, P. Ornetti, and D. Laroche, “Machine learning approaches for ac- tivity recognition and/or activity prediction in locomotion assistive devices—a systematic review,” Sensors, vol. 20, no. 21, 2020

    2020

  8. [16]

    Human posture prediction during physical human-robot interaction,

    L. Vianello, J.-B. Mouret, E. Dalin, A. Aubry, and S. Ivaldi, “Human posture prediction during physical human-robot interaction,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 6046–6053, 2021

    2021

  9. [17]

    Ilosa: Interactive learning of stiffness and attractors,

    G. Franzese, A. M ´esz´aros, L. Peternel, and J. Kober, “Ilosa: Interactive learning of stiffness and attractors,” arXiv preprint arXiv:2103.03099 , 2021

    2021 arXiv

  10. [18]

    An active impedance controller to assist gait in people with neuromuscular diseases: Implementation to the hip joint of the autonomyo exoskeleton,

    A. Ortlieb, R. Baud, T. Tracchia, B. Denkinger, Q. Herzig, H. Bleuler, and M. Bouri, “An active impedance controller to assist gait in people with neuromuscular diseases: Implementation to the hip joint of the autonomyo exoskeleton,” in 2018 7th IEEE International Conference o...

    2018

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.