From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation

Haoyi Shi; Mingxi Su; Nikolaos Papanikolopoulos; Ted Morris; Vassilios Morellas

REVIEW 3 major objections 8 minor 21 references

From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation

T0 review · 3 major / 8 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read This paper claims that a GRU-based variational autoencoder can learn a latent representation of a redundant manipulator's configuration space, and a feed-forward network can map human arm gestures into that space to generate robot…

desk verdict A clean latent-space teleoperation idea whose core claim—generation of novel joint configurations—is never actually measured; the experiments only report end-effector errors, which a redundant arm can satisfy without producing anything new. read the letter →

arxiv 2502.02036 v1 pith:2HRUTHS7 submitted 2025-02-04 cs.RO cs.AI

Haoyi Shi , Mingxi Su , Ted Morris , Vassilios Morellas , Nikolaos Papanikolopoulos This is my paper · ORCID

classification cs.ROcs.AI

keywords teleoperationredundantmanipulatorvariationalautoencodergatedrecurrentunithumanarmgesturemappinglatentconfigurationspaceKinovaGen3imitationlearning

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 establish that a redundant seven-joint robot arm can be controlled by a person's natural arm gestures without solving inverse kinematics or explicitly choosing among the arm's many joint solutions. The authors build a GRU-based variational autoencoder (VAE) that learns a compact latent representation of the robot's configuration space from two-time-step joint trajectories, then train a feed-forward network to map human right-arm joint angles into that latent space. At run time the VAE decoder turns the mapped latent vectors into robot joint trajectories, and because the latent space is continuous, the decoder can produce manipulator configurations that were not part of the training data. Physical experiments with four participants reached three target poses with mean position error 2.51 ± 0.75 cm and mean orientation cosine similarity 0.97 ± 0.01, which the authors take as evidence that the mapping generalizes to operators outside the training set.

What carries the argument

The load-bearing object is the GRU-based variational autoencoder over the manipulator's configuration space. Its input is a two-time-step window of 7-DOF joint angles represented as 14 projected unit values $(\cos d, \sin d)$ to avoid wrap-around singularities; the encoder compresses this window into 10 Gaussian latent features, and the decoder reconstructs and, crucially, generates trajectories from sampled latent vectors. The key design choice is a cyclical sigmoid annealing schedule for the KL-divergence weight $\beta$ in the loss $L_{VAE} = MAE(Input, Reconstr) + \beta KL(q(z|x)\parallel p(z))$, which makes individual latent features correlate strongly with specific robot joints rather than spreading information across all features. That structured latent space is what lets a small fully-connected network learn the human-joint-to-latent mapping, and it is the mechanism by which the decoder can interpolate to novel configurations.

What would settle it

Hold out a subset of the 12 training poses and test whether the decoder can still generate the held-out trajectories when driven by the corresponding human gestures; if the error climbs well above the reported 2.5 cm baseline, the so-called novel configurations are likely memorized interpolations rather than true generation.

Watch

Extended reading notes

Core claim

The central claim is that a variational autoencoder trained on manipulator joint trajectories can learn a latent space that serves as a shared representation between human arm motion and robot arm motion, so human gestures can generate novel robot configurations. Concretely, the paper proposes a GRU-based VAE with a 10-dimensional latent space trained on 15,043 pairs of human right-arm joint configurations and corresponding latent robot configurations, with human joint angles converted to projected unit values and robot joint angles represented as $(\cos d, \sin d)$ pairs. A fully-connected network maps the 24-dimensional human feature vector to the 10-dimensional latent vector, and the VAE decoder outputs two time steps of manipulator joint positions at 10 Hz. The authors support the claim experimentally: four participants, one of whom did not help collect the training data, drove the Kinova Gen3 7-DOF manipulator to three target poses with a mean absolute error of 2.51 ± 0.75 cm and a mean cosine similarity of 0.97 ± 0.01.

Load-bearing premise

The load-bearing premise is that 15,043 paired demonstrations from one operator mimicking 12 robot poses teach a mapping that works for other people and for target-reaching gestures that were never demonstrated.

Editorial extensions

If this is right

Redundant manipulators can be driven without runtime inverse kinematics; the latent representation absorbs the joint-selection problem.
Operators can produce robot configurations that were absent from the training set, which makes the system a practical way to bootstrap imitation-learning demonstrations for new tasks.
The modular pipeline transfers to other robot morphologies by retraining the VAE on that robot's joint trajectories.
Adding joint-velocity information to the latent space, as the authors point out, would make generated motion smoother and more responsive.
The runtime cost is one feed-forward pass through the mapping network plus one decoder pass, light enough for real-time teleoperation.

Reading between the lines

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

The single-operator training set makes cross-user generalization the main open question; an editorial extension would be a hold-one-operator-out evaluation in which the mapping network is trained on all but one participant's data and tested on the held-out participant, separating true generalization from the familiarization effect.
Because the VAE latent space is continuous and the pipeline is modular, a testable extension is to retrain the VAE on a different redundant manipulator's joint trajectories and reuse the same human-arm mapping network to see whether the mapping transfers without additional human data.
The authors' premise that human kinematics only need to be repeatable suggests the interface is co-adaptive; error across a participant's repeated trials should decrease as they learn the robot's response, a trend that could be measured directly from the recorded per-trial times and positions.

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

Short version: the paper has a well-described, sensible idea—learn a latent space of manipulator joint configurations with a GRU-VAE, regress human arm joints into that latent space, and decode—and the physical experiments show the end-effector reaching targets with a few cm error. What it doesn't do is test the claim that makes this more than direct joint regression: that the system generates manipulator configurations unseen in training. The experiments report only end-effector position and orientation error. For a 7-DOF arm, a 6-DOF end-effector target leaves a null space, so the system could be replaying training-set joint sequences and still hit those targets. The stress-test note is right: there is no distance-to-training-set analysis, no coverage measure on the redundancy manifold, no count of generated configurations outside a training neighborhood. That omission is load-bearing, not cosmetic.

Credit where due: the specific combination of a GRU-VAE for the configuration space plus a feed-forward mapping from human arm joints is not in the cited literature, and the architecture choices are reasonable—the sin/cos projection for angular wrap-around is a nice practical touch, and the annealing-schedule correlation analysis in Fig. 4 is suggestive. The paper is clearly written, the physical setup is real, and the limitations section is honest about single-subject training; it even documents one participant compensating with lower-back motion, which is a good sign of genuine reporting.

The soft spots are mostly in the evaluation. Table II has no baseline, and the comparison against a direct fully-connected predictor (Fig. 5) is a single qualitative trajectory plot, not a quantitative study. The cross-user claim is weaker than stated: all four participants are within a few cm of the training subject's height and arm span, so the test population does not actually vary anthropometrics much. The single-operator training set (15,043 pairs after one person mimicked a handful of robot poses) is thin for generalization claims, though the method could plausibly work with more data.

Who is this for: robotics researchers working on teleoperation and learning-based interfaces for redundant manipulators. It's a competent engineering contribution that a good reviewer could push into a much stronger paper by adding a baseline, measuring novelty of generated joint configurations directly (e.g., distance to training set, null-space exploration), and testing with more diverse participants. It deserves peer review, but the current claims outrun the evidence. I'd be skeptical about the strong generality and novelty statements until those gaps are closed.

Referee Report

3 major / 8 minor

Summary. The paper proposes a teleoperation system for a 7-DOF Kinova manipulator in which a GRU-based variational autoencoder learns a low-dimensional latent representation of the manipulator's configuration space, and a fully-connected network maps human arm joint angles into that latent space. The decoder then generates joint trajectories in real time. The system is trained on paired human-robot trajectory data collected from a single operator who first mimicked 12 robot poses, and it is evaluated in a physical target-reaching task with four participants. The paper reports end-effector position and orientation accuracy and claims that the VAE decoder enables the generation of novel manipulator configurations from human gestures not seen during training.

Significance. If the central claim of novel-configuration generation is validated, the approach would offer a practical, learning-based alternative to analytical inverse kinematics for redundant manipulators, with potential benefits for co-robotics and rapid task reprogramming. The paper has several strengths: it presents a complete system implementation, documents model training details, reports IRB approval, and includes a useful analysis of latent-feature disentanglement with a cyclical annealing schedule (Fig. 4). The qualitative comparison against a direct fully-connected regressor (Fig. 5) is a reasonable sanity check. However, the significance is substantially undercut by the absence of any joint-space evaluation that would support the novelty-generation claim, and by the single-subject training set with anthropometrically similar test participants. As it stands, the paper reads as a feasibility study whose central claim is not yet measured.

major comments (3)

[Section VI and Abstract] The central claim that the pipeline 'enables the generation of novel manipulator configurations from human features that were not present during training' is not directly evaluated anywhere in the manuscript. The only quantitative experiment (Section IV, Table II) reports end-effector Euclidean distance and orientation cosine similarity; for a 7-DOF redundant manipulator, a 6-DOF end-effector target leaves a one-dimensional null space of joint configurations, so the reported mean absolute error of 2.51 ± 0.75 cm and cosine similarity of 0.97 ± 0.01 can be achieved even if the decoder always reproduces training-set configurations or learns a fixed kinematic retargeting. No analysis of the generated joint-angle sequences is reported: no distance to the training set, no coverage of the redundancy manifold, and no count of configurations outside a training neighborhood. The authors should add joint-space metrics (e.g., nearest-neighbor distance to training samples, or a novelty/coverage measure) to substantiate the load-bearing claim that distinguishes this method from direct human-to-joint regression.
[Section III-A.3 and Section V] The paired training dataset was collected from a single participant (170 cm) after a short muscle-memory adaptation in which the operator mimicked 12 robot start/end poses. This protocol defines the human-to-latent correspondence implicitly, so the learned mapping may only capture the specific mimicry behavior rather than a generalizable human-to-robot kinematic relation. The Section IV claim to test 'different operator anthropometric upper body measurements' is not supported: the four participants have mean height 169 ± 1.24 cm and mean arm span 173.9 ± 3.0 cm, nearly identical to the training subject, and participant 1 is the training subject. Section V itself documents that participant 3 used lower-back bending to control Joint 0, which demonstrates cross-user variability. To support the generality claim, the authors should recruit participants with substantially different body sizes and movement habits, and report per-participant joint-space behavior rather than only end-effector errors.
[Section IV, Table II] There is no quantitative baseline in the experimental evaluation. The only comparison, Fig. 5, is a qualitative trajectory plot against a fully-connected network without the VAE decoder, and it is not summarized numerically. Additionally, the table header 'Orientation Diff. (radian)' conflicts with the reported values, which are cosine similarities (dimensionless), and the caption warns of rounding in the standard error only for the '0.00' entries. The text's 'mean absolute error 2.51 ± 0.75 cm' also does not match the 'All' row of Table II, which reports 2.73 ± 0.95 cm. Please clarify how the orientation cosine similarity is computed (on the full rotation matrix, quaternion, or a single axis), report a numerical baseline such as direct regression or analytical IK, and reconcile the aggregate numbers reported in the text with those in the table.

minor comments (8)

[Abstract and Section VI] The abstract uses 'from human gestures' while the introduction and conclusion use 'human features'; please make the terminology consistent.
[Section III-A.3] The phrase 'muscle memory' is informal; please define it more precisely as a short adaptation period in which the operator practiced mimicking the robot motions.
[Fig. 5] The trajectory plot lacks axis labels, units, and a time reference, which makes it difficult to interpret the claimed qualitative advantage of the VAE-based pipeline.
[Introduction] The text says 'Gate Recurrent Unit' in the introduction; the standard term is 'Gated Recurrent Unit' (GRU).
[References] Several references are incomplete, for example [2] lacks a journal name and volume/pages; please supply full bibliographic details for all entries.
[Section III-B.3] The reported batch size of 1,024,000 is larger than the dataset size of 15,043 pairs; please verify whether full-batch training was intended or whether this is a typo.
[Fig. 4] The Z-axis label says 'Correlation-Coefficient score' but the color scale is not defined; please clarify the range and meaning of the correlation values.
[Section IV] Please clarify whether 'participant 1 engages in training data collection but has not been practiced' means they did not receive the ten-minute familiarization phase, since this affects interpretation of their results.

Circularity Check

2 steps flagged · score 4.0 of 10

The 'novel configuration' claim is supported by a self-referential data protocol and human-in-the-loop evaluation, not by an independent test of the learned mapping.

self definitional [Section III-A.2 and III-A.3 (Kinematic Mapping; Human Arm Joints Configuration Dataset Collection)]
"a kinematic correlation between the manipulator and the human arm was first defined before the model training and data collection. ... the human operator observes the kinematic motion of the robot manipulator and attempts to mimic the motion with corresponding joints as described in Fig. 1."

The paired input 'human arm configurations' are not independent samples; they are produced by having the operator reproduce the robot's demonstrated joint motions under an author-defined joint-to-joint map. The MLP is then trained to map these imitation-derived human features to the robot latent codes. Any 'novel manipulator configuration' generated at test time is therefore a decode of the same defined correspondence plus VAE interpolation, so the claimed generation from human features is, at least in the training distribution, a restatement of the data-collection protocol rather than an emergent model capability.
fitted input called prediction [Section IV (Experiment and Results) and Section III-A.3]
"Then, we provide each participant ten minutes to move their arm to become familiar with the relation between their arm joint angles and the Kinova joint angles. ... the measured human kinematics only require to be repeatable since the human can adjust their visuo-proprioceptive queues according to context and nature of the task to be completed."

The evaluation metric is end-effector distance and orientation reached when the participant teleoperates the arm. Because the participant is free to adjust their arm angles in real time with visual feedback after a familiarization period, the human input is fitted to drive the robot to the target. The reported MAE/cosine similarity therefore measures the participant's ability to invert the fixed network mapping, not the model's capacity to predict or generate novel configurations. The conclusion 'This supports the generality and accuracy of the proposed system' attributes this closed-loop human adaptation to the model.

full rationale

The paper's core derivation is a straightforward composition: a GRU-VAE learns a latent space from robot trajectories, and an MLP maps human arm angles into that latent space. There is no equation-level circularity in this training pipeline, and no load-bearing self-citation. However, the paper's central claim—that the system 'enables the generation of novel manipulator configurations from human features that were not present during training'—is undermined by two protocol-level circularities. First, the training human features are manufactured by having the operator imitate the robot's joint motions according to a manually defined kinematic correspondence, so the learned mapping is partly a rediscovery of that experimental instruction rather than an independent embodiment result. Second, the physical experiments let each participant adapt their arm motions to the fixed mapping for ten minutes and then use closed-loop visual feedback during the trials; the reported end-effector accuracy is therefore a measure of human adaptation, not a validation that the model generalizes to novel configurations or novel users. These issues make the novelty claim self-referential in its evidence, while leaving the actual VAE-based pipeline plausible as an engineering system. Score 4 reflects partial circularity in the evidential chain, not a fully forced derivation.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claims rest on several domain assumptions about kinematic equivalence between the human arm and the 7-DOF robot, the representativeness of MoveIt-generated trajectories, and the reliability of IMU-based human motion capture. These are not validated within the paper.

free parameters (6)

Latent feature size z = 10
Selected after 'multiple experiments'; controls the dimensionality of the configuration-space encoding.
GRU hidden feature size = 28
Chosen by experimentation for the encoder/decoder GRU layers.
Max KL weight beta = 0.1
Annealing schedule upper bound set manually to balance reconstruction and regularization.
Annealing schedule cycles = 4 sigmoid cycles
Chosen to prevent KL vanishing while allowing latent features to become joint-specific.
VAE batch size = 1,024,000 (as written)
Reported batch size is unusually large; regardless, it is a hand-set training hyperparameter.
Learning rates = 1e-4 (VAE), 1e-3 (MLP)
Adam optimizer settings; not derived from data.

assumptions (4)

domain assumption The human arm can be represented as a 7-DOF kinematic chain with the joint-to-joint correspondence shown in Fig. 1 (shoulder to J1-J3, elbow to J4, wrist to J5-J7).
The entire data pairing and retargeting pipeline depends on this mapping, which is asserted without kinematic validation.
domain assumption MoveIt-generated trajectories from random 6D end-effector poses cover the feasible configuration space of the Kinova arm.
The VAE is trained solely on these simulated trajectories; unrepresentative sampling would bias the latent space.
domain assumption Xsens Awinda IMU tracking, after proprietary calibration, provides joint angles repeatable enough for both training labels and live teleoperation input.
The paper states that repeatability (not absolute accuracy) is the requirement, but reports no validation of sensor accuracy.
domain assumption A VAE with a standard Gaussian prior and MAE reconstruction loss can adequately approximate the manipulator's configuration space.
This is the core generative-modeling assumption; no ablation is provided beyond the annealing comparison in Fig. 4.

how reviews work

0 comments

Cite this review

Pith. "Pith review of From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation." pith.science (2026). https://pith.science/paper/2HRUTHS7

@misc{pith2026250202036,
  author       = {Pith},
  title        = {Pith review of: From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2HRUTHS7}},
  note         = {Machine review of arXiv:2502.02036}
}

read the original abstract

This paper presents a teleoperation system for controlling a redundant degree of freedom robot manipulator using human arm gestures. We propose a GRU-based Variational Autoencoder to learn a latent representation of the manipulator's configuration space, capturing its complex joint kinematics. A fully connected neural network maps human arm configurations into this latent space, allowing the system to mimic and generate corresponding manipulator trajectories in real time through the VAE decoder. The proposed method shows promising results in teleoperating the manipulator, enabling the generation of novel manipulator configurations from human features that were not present during training.

Figures

Figures reproduced from arXiv: 2502.02036 by the authors.

**Figure 1.** Orange labels, J1-J7, indicate the positions of the Kinova manipulator’s joints. Green labels, q1 -q7 , indicate the positions of the human arm’s joints. Arrow shows the mapping relationship between the manipulator’s joint and the kinematic chain of a human upper limb. the real-time end-effector’s position and orientation with external devices with a 6 degrees of freedom (DOF) robot with the joint trajectory space … view at source ↗

**Figure 1.** This process facilitates proprioceptive neuromuscular [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** The GRU-based VAE takes a 2-time-step manipulator joint angle [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Figures from the paper (4 more)

**Figure 3.** Figure 3: The fully-connected module consists of an input layer, and three [PITH_FULL_IMAGE:figures/full_fig_p004_3.png]

**Figure 5.** Figure 5: The red trajectory: operator’s hand position in Cartesian space. The green: proposed teleoperation pipeline. The blue: fullyconnected network without the VAE decoder. Theoretically, the VAE decoder approximates the likelihood distribution function P(x | z) that enabl…

**Figure 4.** Figure 4: This figure demonstrates the training result of GRU-based VAE with [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 6.** Figure 6: Experiment setup. The operator needs to teleoperate the Kinova Gen [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 16 canonical work pages

[1]

Affordance learning for end-to-end visuomotor robot control,

A. Hamalainen, K. Arndt, A. Ghadirzadeh, and V . Kyrki, “Affordance learning for end-to-end visuomotor robot control,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 1781–1788, IEEE

work page 2019
[2]

Human-oriented control for haptic teleoper- ation,

S. Hirche and M. Buss, “Human-oriented control for haptic teleoper- ation,” vol. 100, no. 3, pp. 623–647

work page
[3]

Design and evaluation of an intuitive haptic teleoperation control system for 6-DoF industrial manipulators,

I. Dekker, K. Kellens, and E. Demeester, “Design and evaluation of an intuitive haptic teleoperation control system for 6-DoF industrial manipulators,” vol. 12, no. 2, p. 54. Number: 2 Publisher: Multidis- ciplinary Digital Publishing Institute

work page
[4]

Real-time hand tracking with a color glove for the actuation of anthropomorphic robot hands,

M. Schr ¨oder, C. Elbrechter, J. Maycock, R. Haschke, M. Botsch, and H. Ritter, “Real-time hand tracking with a color glove for the actuation of anthropomorphic robot hands,” in 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012) , pp. 262–269. ISSN: 2164-0580

work page 2012
[5]

Task- oriented hand motion retargeting for dexterous manipulation imitation,

D. Antotsiou, G. Garcia-Hernando, and T.-K. Kim, “Task- oriented hand motion retargeting for dexterous manipulation imitation,” arXiv preprint arXiv:1810.01845 . Available at http://arxiv.org/abs/1810.01845

arXiv
[6]

Gesture recognition for hu- manoid robot teleoperation,

I. Ajili, M. Mallem, and J.-Y . Didier, “Gesture recognition for hu- manoid robot teleoperation,” in 2017 26th IEEE International Sym- posium on Robot and Human Interactive Communication (RO-MAN) , pp. 1115–1120. ISSN: 1944-9437

work page 2017
[7]

AnyTeleop: A general vision-based dexterous robot arm-hand teleoperation system,

Y . Qin, W. Yang, B. Huang, K. Van Wyk, H. Su, X. Wang, Y .-W. Chao, and D. Fox, “AnyTeleop: A general vision-based dexterous robot arm-hand teleoperation system,” arXiv preprint arXiv:2307.04577 . Available at http://arxiv.org/abs/2307.04577

arXiv
[8]

Baxter’s homunculus: Virtual reality spaces for teleoperation in manufacturing,

J. I. Lipton, A. J. Fay, and D. Rus, “Baxter’s homunculus: Virtual reality spaces for teleoperation in manufacturing,” vol. 3, no. 1, pp. 179–186. Conference Name: IEEE Robotics and Automation Letters

work page

Show all 21 references

[9]

Anytime, anywhere: Human arm pose from smartwatch data for ubiquitous robot control and teleoperation,

F. C. Weigend, S. Sonawani, M. Drolet, and H. B. Amor, “Anytime, anywhere: Human arm pose from smartwatch data for ubiquitous robot control and teleoperation,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 3811–3818. ISSN: 2153-0866

2023
[10]

Haptic- guided teleoperation of a 7-DoF collaborative robot arm with an identical twin master,

J. Singh, A. R. Srinivasan, G. Neumann, and A. Kucukyilmaz, “Haptic- guided teleoperation of a 7-DoF collaborative robot arm with an identical twin master,” vol. 13, no. 1, pp. 246–252
[11]

A heterogeneous master-slave tele- operation method for 7-DOF manipulator,

R. Su, K. Xu, L. Zhao, and P. Yu, “A heterogeneous master-slave tele- operation method for 7-DOF manipulator,” in 2021 China Automation Congress (CAC), pp. 1740–1744. ISSN: 2688-0938

2021
[12]

As- sessment of the accuracy of a human arm model with seven degrees of freedom,

R. Prokopenko, A. Frolov, E. Biryukova, and A. Roby-Brami, “As- sessment of the accuracy of a human arm model with seven degrees of freedom,” vol. 34, pp. 177–85
[13]

Robust real- time whole-body motion retargeting from human to humanoid,

L. Penco, B. Clement, V . Modugno, E. Mingo Hoffman, G. Nava, D. Pucci, N. G. Tsagarakis, J. B. Mouret, and S. Ivaldi, “Robust real- time whole-body motion retargeting from human to humanoid,” in 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 4...

2018
[14]

Reduced- complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation,

A. Ajoudani, C. Fang, N. Tsagarakis, and A. Bicchi, “Reduced- complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation,” vol. 37, no. 1, pp. 155–167. Publisher: SAGE Publications Ltd STM
[15]

Deep neural network approach in human-like redundancy optimiza- tion for anthropomorphic manipulators,

H. Su, W. qi, C. Yang, A. Aliverti, G. Ferrigno, and E. De Momi, “Deep neural network approach in human-like redundancy optimiza- tion for anthropomorphic manipulators,” vol. PP, pp. 1–1
[16]

Human to robot whole-body motion transfer,

M. Arduengo, A. Arduengo, A. Colom ´e, J. Lobo-Prat, and C. Torras, “Human to robot whole-body motion transfer,” in2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids) , pp. 299–
[17]

Auto-encoding variational bayes,

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 . Available at http://arxiv.org/abs/1312.6114

arXiv
[18]

Generating sentences from a contin- uous space,

S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Joze- fowicz, and S. Bengio, “Generating sentences from a contin- uous space,” arXiv preprint arXiv:1511.06349 . Available at http://arxiv.org/abs/1511.06349

arXiv
[19]

Embodied language learning with paired variational autoencoders,

O. ¨Ozdemir, M. Kerzel, and S. Wermter, “Embodied language learning with paired variational autoencoders,” in 2021 IEEE International Conference on Development and Learning (ICDL) , pp. 1–6

2021
[20]

Xsens MVN: Consistent tracking of human motion using inertial sensing,

M. Schepers, M. Giuberti, and G. Bellusci, “Xsens MVN: Consistent tracking of human motion using inertial sensing,” 03 2018. Available at https://www.movella.com/products/motion-capture

2018
[21]

Cyclical annealing schedule: A simple approach to mitigating KL vanishing,

H. Fu, C. Li, X. Liu, J. Gao, A. Celikyilmaz, and L. Carin, “Cyclical annealing schedule: A simple approach to mitigating KL vanishing,” arXiv preprint arXiv:1903.10145 . Available at http://arxiv.org/abs/1903.10145

1903 arXiv

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

[1] [1]

Affordance learning for end-to-end visuomotor robot control,

A. Hamalainen, K. Arndt, A. Ghadirzadeh, and V . Kyrki, “Affordance learning for end-to-end visuomotor robot control,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 1781–1788, IEEE

work page 2019

[2] [2]

Human-oriented control for haptic teleoper- ation,

S. Hirche and M. Buss, “Human-oriented control for haptic teleoper- ation,” vol. 100, no. 3, pp. 623–647

work page

[3] [3]

Design and evaluation of an intuitive haptic teleoperation control system for 6-DoF industrial manipulators,

I. Dekker, K. Kellens, and E. Demeester, “Design and evaluation of an intuitive haptic teleoperation control system for 6-DoF industrial manipulators,” vol. 12, no. 2, p. 54. Number: 2 Publisher: Multidis- ciplinary Digital Publishing Institute

work page

[4] [4]

Real-time hand tracking with a color glove for the actuation of anthropomorphic robot hands,

M. Schr ¨oder, C. Elbrechter, J. Maycock, R. Haschke, M. Botsch, and H. Ritter, “Real-time hand tracking with a color glove for the actuation of anthropomorphic robot hands,” in 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012) , pp. 262–269. ISSN: 2164-0580

work page 2012

[5] [5]

Task- oriented hand motion retargeting for dexterous manipulation imitation,

D. Antotsiou, G. Garcia-Hernando, and T.-K. Kim, “Task- oriented hand motion retargeting for dexterous manipulation imitation,” arXiv preprint arXiv:1810.01845 . Available at http://arxiv.org/abs/1810.01845

arXiv

[6] [6]

Gesture recognition for hu- manoid robot teleoperation,

I. Ajili, M. Mallem, and J.-Y . Didier, “Gesture recognition for hu- manoid robot teleoperation,” in 2017 26th IEEE International Sym- posium on Robot and Human Interactive Communication (RO-MAN) , pp. 1115–1120. ISSN: 1944-9437

work page 2017

[7] [7]

AnyTeleop: A general vision-based dexterous robot arm-hand teleoperation system,

Y . Qin, W. Yang, B. Huang, K. Van Wyk, H. Su, X. Wang, Y .-W. Chao, and D. Fox, “AnyTeleop: A general vision-based dexterous robot arm-hand teleoperation system,” arXiv preprint arXiv:2307.04577 . Available at http://arxiv.org/abs/2307.04577

arXiv

[8] [8]

Baxter’s homunculus: Virtual reality spaces for teleoperation in manufacturing,

J. I. Lipton, A. J. Fay, and D. Rus, “Baxter’s homunculus: Virtual reality spaces for teleoperation in manufacturing,” vol. 3, no. 1, pp. 179–186. Conference Name: IEEE Robotics and Automation Letters

work page