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REVIEW 3 major objections 6 minor 3 cited by

TelePreview: A User-Friendly Teleoperation System with Virtual Arm Assistance for Enhanced Effectiveness

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read TelePreview claims that letting operators visualize and refine a robot's next movement in an aligned virtual preview before physical execution improves teleoperation success rates and safety while keeping hardware under $1,000.

desk verdict Solid teleoperation system with a genuinely useful preview concept, but the headline outperformance claim is propped up by non-comparable cross-paper numbers and a couple of internal inconsistencies. read the letter →

arxiv 2412.13548 v6 pith:QXHXYVHW submitted 2024-12-18 cs.RO cs.HC

Jingxiang Guo , Jiayu Luo , Zhenyu Wei , Yiwen Hou , Zhixuan Xu , Xiaoyi Lin , Chongkai Gao , Lin Shao This is my paper · ORCID
classification cs.ROcs.HC
keywords teleoperationvirtualpreviewaugmentedrealitydexterousmanipulationimitationlearningSMPL-Xmotionretargetinglow-costrobotics
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 presents TelePreview, a teleoperation system that overlays a spatially aligned virtual robot on the real scene so an operator can see the outcome of the next movement before the physical robot executes it. The authors aim to show that this preview-then-execute design makes fine-grained manipulation tasks easier for new users, safer for hardware, and cheaper than existing dexterous teleoperation systems, with a hardware cost under $1,000. They report higher success rates than three published teleoperation systems across five real-world tasks, and a user study in which enabling the preview raised new-user success rates and cut execution times. If the claims hold, the system offers a low-cost route to cleaner demonstration data for imitation learning, because exploratory moves are filtered out before they reach the robot.

What carries the argument

The central object is the preview overlay: a virtual robot rendered from a 3D model and registered to the physical robot through AprilTag-based hand-eye calibration, then alpha-blended into live camera images from multiple viewpoints. A foot pedal toggles between Preview Mode, where the virtual arm moves with the user's live hand while the physical robot is frozen, and Align Mode, where the final preview pose becomes the target for a motion-planning library and the physical robot executes it autonomously. Supporting this is a teleoperation pipeline that maps IMU-tracked wrist pose and mocap-glove hand joints onto the robot through the SMPL-X body model, a linear joint-to-joint mapping with per-joint scale, bias, and direction, and a learned self-collision prediction/correction network that keeps high-degree-of-freedom hand configurations valid.

What would settle it

Run the same five manipulation tasks on the same robot, with the same objects, same environment, and same five new users, using TelePreview, Open Teach, AnyTeleop, and Telekinesis under an identical protocol; if TelePreview does not achieve higher success rates or shorter execution times than the baselines, the paper's headline comparison fails.

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Extended reading notes

Core claim

TelePreview's central claim is that inserting a virtual preview step between human command and robot execution—rendered as a robot arm aligned with the physical arm via AprilTag calibration, with a foot pedal toggling between preview-only and align modes—lets operators issue refined, collision-free commands. The paper argues this separation of exploration from execution reduces self-collisions, shortens task times, and improves success rates, and that the underlying SMPL-X based retargeting pipeline makes the system portable across input devices and end-effectors with only small parameter changes. The evidence is success-rate comparisons on five manipulation tasks (Pick & Place, Pour, Hang, Box Rotation, Cup Stacking) against Open Teach, AnyTeleop, and Telekinesis, plus a within-system user study comparing preview enabled versus disabled. The paper frames the preview as a way to capture only intentional, task-oriented motions in demonstration data, avoiding the exploratory movements that degrade imitation-learning policies.

Load-bearing premise

The claim that TelePreview outperforms other teleoperation systems depends on assuming that the success rates quoted from the Open Teach, AnyTeleop, and Telekinesis papers are directly comparable to TelePreview's numbers, even though each was measured on different hardware, objects, and operators.

Editorial extensions

If this is right

  • If the preview works as claimed, new users can produce task-ready demonstrations with less practice, lowering the barrier to collecting imitation-learning data.
  • Separating exploration from execution yields cleaner demonstration trajectories, which should improve the generalization of learned manipulation policies.
  • Sub-$1,000 hardware with modular input and output support makes the system feasible for research labs with limited budgets.
  • The preview-to-execute workflow protects robot hardware from self-collisions and unsafe commands during training data collection.
  • Multi-view rendering and AprilTag alignment could transfer to other camera-based augmented reality interfaces for robot teleoperation.

Reading between the lines

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

  • The paper does not test bimanual or mobile manipulators, so extending the preview design to those settings is an untested consequence of the architecture.
  • An apples-to-apples replication with identical operators, hardware, and objects may shrink the reported margin over baselines, because the current comparison uses numbers quoted from different papers.
  • Replacing alpha blending with depth-aware rendering could automatically resolve the occlusion ambiguities the paper identifies, potentially improving new-user performance further.
  • The claim that previewing yields cleaner demonstrations rests on the assumption that exploratory motions during preview are not recorded; the paper reports execution-time improvements but does not directly measure downstream imitation-learning accuracy.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper introduces TelePreview, a teleoperation system that overlays a spatially aligned virtual robot on the real scene so that an operator can preview and refine commands before physical execution. The system combines IMU-based wrist tracking, mocap glove hand tracking, an SMPL-X abstraction layer, and a learned collision-avoidance retargeting module (CPN/CCN), with a reported hardware cost of under $1,000. The experiments address three questions: (Q1) effectiveness versus prior teleoperation systems, (Q2) benefit of the preview for new users, and (Q3) adaptability to different end-effectors. The evaluation includes success rates and execution times on five real-world manipulation tasks, a user study with 5 new users comparing with and without preview, and deployment on a dexterous hand, a parallel-jaw gripper, and a vacuum gripper. The paper claims that TelePreview outperforms other teleoperation systems across the five tasks and that preview assistance boosts new-user success and speed.

Significance. If the claims are established, TelePreview would be a useful, low-cost teleoperation interface that improves data-collection quality by letting operators separate exploration from execution. The paper has several genuine strengths: it releases code and deployment documentation, gives a detailed bill of materials, and runs a same-protocol internal comparison (with vs. without preview) that mostly shows consistent gains in success rate and execution time for new users. The multi-view preview design is a practical contribution, and the adaptation to three end-effectors with minimal parameter changes is well motivated. However, the headline claim of outperforming prior systems is not supported by the presented evidence, because the baseline success rates are quoted from other papers with different hardware, tasks, objects, operators, and success criteria. The internal user study is the strongest evidence, but its per-task claims are not uniformly consistent with the data.

major comments (3)
  1. [Section VI-A, Table I] The claim that TelePreview achieves "higher success rates than all baselines" rests on success rates quoted from other papers (Open Teach [1], AnyTeleop [3], Telekinesis [32]), as stated in Section VI-A: "We reference the baseline success rates from their papers." These numbers were measured on different robot hands, objects, task definitions, operators, and success criteria, so they are not comparable to the TelePreview numbers in Table I. Without matched-protocol baseline runs on the same hardware and tasks, Table I only shows that TelePreview works well in its own setup, not that it outperforms prior systems. Moreover, the table shows AnyTeleop also achieving 1.0 on Pick & Place, so TelePreview does not strictly exceed all baselines even under the authors' own tabulation.
  2. [Section VI-E, Table II] The statement that "success rates increase across all tasks" is contradicted by the Pour task, where the success rate drops from 0.9 (w/o preview) to 0.8 (w/ preview), a difference of -0.1. In addition, the Hang task shows a small increase in execution time (+1.53 s) when the preview is enabled. While the average across tasks favors the preview, the per-task claims are not consistently supported. With only 5 new users and no statistical significance testing, the strength of the Q2 conclusion should be tempered to reflect the observed variability.
  3. [Section IV-C, Appendix A] The collision-avoidance component (CPN and CCN) is described as a key part of the teleoperation pipeline, but no experiment quantifies its contribution. In particular, there is no ablation showing collision rates during teleoperation with and without the network, nor a comparison of task success with and without the correction step. The appendix reports that baseline methods suffered self-collisions in the authors' reproduction attempts, but this is anecdotal and not a controlled measurement. Without such an evaluation, the "non-collision retargeting" property and its role in the reported task success rates remain unverified.
minor comments (6)
  1. [Section II-A] The sentence "But Many existing teleoperation frameworks" should be "However, many existing teleoperation frameworks".
  2. [Section V-B.3] The motion planning library "mplib" is used without a citation or reference; please add the appropriate bibliographic entry.
  3. [Table II] The header "Average Success" is ambiguous; consider renaming it to "Success Rate (mean)" to clarify that it is the mean over 10 trials.
  4. [Table III] There are spacing artifacts in the table (e.g., "13 .6" and "14 .2"); also, the LeapHand row duplicates the Pick & Place numbers from Table II, and the user population (new users vs. the general group) should be stated explicitly in the caption.
  5. [Appendix D, Figures 9-10] The captions should identify the "baseline vision-based method [3]" specifically as AnyTeleop, and state how many participants provided the practice-time data.
  6. [Section IV-A, Eq. (1)] The world frame is defined as the midpoint between the feet, but Eq. (1) uses pw(0) as the initial wrist position; please clarify whether pw(0) and pe(0) are recorded at the same time and how the initial end-effector pose is obtained.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: preview benefit and collision networks are experimentally and independently grounded; cross-paper baseline comparison is a validity concern, not circularity.

full rationale

TelePreview's central claims are empirical rather than derived from fitted inputs. The preview benefit in Table II is an experimental contrast measured on the same platform, and the collision networks (CPN/CCN) are trained on labels produced by forward kinematics plus an independent collision checker (Appendix A), so the network outputs are not wired to the success metric. No equation in the paper defines a predicted quantity in terms of the outcome it is used to support, and no load-bearing premise is imported from a self-citation. The closest concern is Section VI-A's statement "We reference the baseline success rates from their papers," which means Table I compares TelePreview's numbers with cross-paper results from different hardware and protocols; that is an external-validity limitation, not circularity. Likewise, the text's claim that "Success rates increase across all tasks" is contradicted by the Pour row in Table II (-0.1) and by the Hang execution time (+1.53 s), but those are internal-consistency or correctness issues rather than circular reductions. Under the paper's own equations, no step reduces to its inputs, so the circularity score is 0.

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

Most of TelePreview's functionality rests on standard robotics components (SMPL-X, AprilTags, FK, MLPs) plus several manually set mapping parameters. No new physical or mathematical entities are introduced; the 'preview robot' is a rendered virtual overlay, not a postulated mechanism. The main uncharged assumption is that published baseline success rates are directly comparable to the authors' own measurements, which drives the headline performance claim.

free parameters (5)
  • Hand mapping scale s_i and bias b_i = not reported numerically
    Eq. (3) maps each glove joint to a LEAP hand joint through fi(x)=si(x-bi)ri; these per-joint values must be calibrated for the retargeting to work.
  • Glove-to-robot joint correspondences k_i = manually picked indices
    Eq. (2) selects which glove joint drives each robot joint, a manual choice based on intuition and task requirements.
  • CCN loss weights alpha and beta = grid-searched, not reported
    Eq. (8) balances pose preservation LMSE against collision probability LCollision; authors state these are tuned by grid search.
  • Vacuum gripper activation threshold = not specified
    Appendix E applies a threshold to the glove signal to produce binary grasp/release for the vacuum gripper; the threshold is hand set.
  • End-effector kinematic transform parameters = not specified
    Appendix E says adapting to grippers requires adjusting the kinematic model and end-effector transform, so these are hardware-specific fitted values.
assumptions (5)
  • domain assumption SMPL-X is a consistent kinematic representation for converting input devices into robot commands.
    Section IV-A.1 adopts SMPL-X as the standard body representation; the wrist and hand poses are expressed in SMPL-X joint frames before retargeting.
  • domain assumption AprilTag detection and hand-eye calibration give an alignment accurate enough for the virtual robot to match the physical robot.
    Section V-B.1 uses AprilTag poses to compute camera-to-robot transforms; the preview's usefulness depends on this spatial alignment being correct.
  • domain assumption Collision labels generated by forward kinematics and a collision detector are correct supervision for the CPN.
    Appendix A.1 trains the self-collision prediction network on sampled configurations labeled by an external collision detection algorithm; wrong labels would make the safety filter unreliable.
  • ad hoc to paper Published baseline success rates are comparable to this paper's success rates.
    Section VI-A says 'We reference the baseline success rates from their papers,' and Table I uses those numbers as the comparison, assuming cross-paper comparability.
  • domain assumption The final preview pose, when handed to the motion planner, produces physical execution matching the preview.
    Section V-B.3 extracts the last preview configuration as the target and plans an 'optimal trajectory' with mplib, assuming the kinematic model used for rendering matches the real robot.

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Cite this review

Pith. "Pith review of TelePreview: A User-Friendly Teleoperation System with Virtual Arm Assistance for Enhanced Effectiveness." pith.science (2026). https://pith.science/paper/QXHXYVHW

@misc{pith2026241213548,
  author       = {Pith},
  title        = {Pith review of: TelePreview: A User-Friendly Teleoperation System with Virtual Arm Assistance for Enhanced Effectiveness},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QXHXYVHW}},
  note         = {Machine review of arXiv:2412.13548}
}
abstract

Teleoperation provides an effective way to collect robot data, which is crucial for learning from demonstrations. In this field, teleoperation faces several key challenges: user-friendliness for new users, safety assurance, and transferability across different platforms. While collecting real robot dexterous manipulation data by teleoperation to train robots has shown impressive results on diverse tasks, due to the morphological differences between human and robot hands, it is not only hard for new users to understand the action mapping but also raises potential safety concerns during operation. To address these limitations, we introduce TelePreview. This teleoperation system offers real-time visual feedback on robot actions based on human user inputs, with a total hardware cost of less than $1,000. TelePreview allows the user to see a virtual robot that represents the outcome of the user's next movement. By enabling flexible switching between command visualization and actual execution, this system helps new users learn how to demonstrate quickly and safely. We demonstrate that it outperforms other teleoperation systems across five tasks, emphasize its ease of use, and highlight its straightforward deployment across diverse robotic platforms. We release our code and a deployment document on our website https://nus-lins-lab.github.io/telepreview-web/.

Figures

Figures reproduced from arXiv: 2412.13548 by the authors.

Figure 1
Figure 1. TelePreview is a user-friendly teleoperation system enabling the real-time virtual preview before robot execution. Abstract— Teleoperation provides an effective way to collect robot data, which is crucial for learning from demonstrations. In this field, teleoperation faces several key challenges: user￾friendliness for new users, safety assurance, and transferability across different platforms. While collecting real … view at source ↗
Figure 2
Figure 2. Overview of the System Architecture (1) Various input devices for capturing human motion. Only one device from each of the two input groups (wrist pose and hand gesture) is required during operation. The VR headset is shown at the intersection of the two groups, as it can capture both; (2) A processing pipeline based on SMPL-X that performs joint mapping and collision-free retargeting; (3) Output to various robot pl… view at source ↗
Figure 3
Figure 3. Pipeline of Our Modules: The system tracks user wrist and hand poses, maps them to robot joint configurations through joint-to-joint mapping and non-collision retargeting, and provides a visual preview before physical execution. We achieve precise alignment between virtual and physical robots through AprilTag calibration. and other perception issues. The keypoint-based retargeting step can be computationally expensi… view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Comparison of Hand Configuration Retargeting Methods: (a) shows the direct mapping between human and robot hands leading to self-collision; (b) demonstrates our collision-aware retargeting approach that maintains safe configurations. 2) A Configuration Correction Netwo…
Figure 5
Figure 5. Figure 5: Our Transformation Relationship: The number in the circle denotes the order of transformation acquisition. As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Rendering and Multi-view Visualization System. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: State Transition of Preview Control VI. EXPERIMENTS Our experiments aim to address the following questions: Q1: How effective is TelePreview vs. Baselines? Q2: How beneficial is TelePreview for new users? Q3: How adaptable is TelePreview for new hardware? A. Experiment…
Figure 8
Figure 8. Figure 8: Pipeline of the Non-collision Module [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: User-reported workload and performance across five [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Practice time. New users reached task readiness [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: Deployment on Different Robots. To demonstrate the generalizability of our system, we deployed TelePreview on a Ufactory xArm robot equipped with three different end-effectors(See in [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: Pick&Place Visualization (a) (b) (c) (d) (e) [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: Hang Visualization (a) (b) (c) (d) (e) [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]
Figure 14
Figure 14. Figure 14: Pour Visualization (a) (b) (c) (d) (e) [PITH_FULL_IMAGE:figures/full_fig_p013_14.png]
Figure 15
Figure 15. Figure 15: Box Rotation Visualization (a) (b) (c) (d) (e) [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]
Figure 16
Figure 16. Figure 16: Cupstack Visualization [PITH_FULL_IMAGE:figures/full_fig_p013_16.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AHEAD: Anticipatory Hand-Driven Teleoperation via Human Intent Prediction

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A VR teleoperation system predicts the operator's intended grasp object and placement slot from recent hand and head motion, letting the robot start moving before the command is completed.

  2. BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots

    cs.RO 2025-08 conditional novelty 6.0 of 10

    BEAVR provides an open-source, low-cost VR teleoperation pipeline for multiple robot embodiments, with LeRobot-format data recording and compatibility with ACT, Diffusion Policy, and SmolVLA.

  3. TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types

    cs.RO 2025-07 conditional novelty 6.0 of 10

    A type-guided teleoperation system that selects predefined dexterous hand poses with a language model outperforms retargeting-based teleoperation on nine real-world tasks and improves imitation learning success.

Reference graph

Works this paper leans on

49 extracted references · 32 canonical work pages · cited by 3 Pith papers

  1. [1]

    Open teach: A versatile teleoperation system for robotic manipulation,

    A. Iyer, Z. Peng, Y . Dai, I. Guzey, S. Haldar, S. Chintala, and L. Pinto, “Open teach: A versatile teleoperation system for robotic manipulation,” arXiv preprint arXiv:2403.07870 , 2024

    arXiv 2024

  2. [3]

    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 , 2023

    arXiv 2023

  3. [32]

    Robotic telekinesis: Learning a robotic hand imitator by watching humans on youtube,

    A. Sivakumar, K. Shaw, and D. Pathak, “Robotic telekinesis: Learning a robotic hand imitator by watching humans on youtube,” arXiv preprint arXiv:2202.10448, 2022

    arXiv 2022

  4. [2]

    Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation,

    Z. Fu, T. Z. Zhao, and C. Finn, “Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation,” arXiv preprint arXiv:2401.02117, 2024

    arXiv 2024

  5. [4]

    Bimanual dexterity for complex tasks,

    K. Shaw, Y . Li, J. Yang, M. K. Srirama, R. Liu, H. Xiong, R. Men- donca, and D. Pathak, “Bimanual dexterity for complex tasks,” arXiv preprint arXiv:2411.13677, 2024

    arXiv 2024

  6. [5]

    Using apple vision pro to train and control robots,

    Y . Park and P. Agrawal, “Using apple vision pro to train and control robots,” 2024

    work page 2024

  7. [6]

    Dexcap: Scalable and portable mocap data collection system for dexterous manipulation,

    C. Wang, H. Shi, W. Wang, R. Zhang, L. Fei-Fei, and C. K. Liu, “Dexcap: Scalable and portable mocap data collection system for dexterous manipulation,” arXiv preprint arXiv:2403.07788 , 2024

    arXiv 2024

  8. [7]

    Open-television: Teleoperation with immersive active visual feedback,

    X. Cheng, J. Li, S. Yang, G. Yang, and X. Wang, “Open-television: Teleoperation with immersive active visual feedback,” arXiv preprint arXiv:2407.01512, 2024

    arXiv 2024

Show all 49 references
  1. [8]

    Manipulability opti- mization for multi-arm teleoperation,

    F. Kennel-Maushart, R. Poranne, and S. Coros, “Manipulability opti- mization for multi-arm teleoperation,” in ICRA 2021 . IEEE, 2021, pp. 3956–3962

    2021

  2. [9]

    Human-like dexterous manipulation for anthropomorphic five-fingered hands: A review,

    Y . Huang, D. Fan, H. Duan, D. Yan, W. Qi, J. Sun, Q. Liu, and P. Wang, “Human-like dexterous manipulation for anthropomorphic five-fingered hands: A review,” Biomimetic Intelligence and Robotics , p. 100212, 2025

    2025

  3. [10]

    Teleoperation of humanoid robots: A survey,

    K. Darvish, L. Penco, J. Ramos, R. Cisneros, J. Pratt, E. Yoshida, S. Ivaldi, and D. Pucci, “Teleoperation of humanoid robots: A survey,” IEEE Transactions on Robotics , vol. 39, no. 3, pp. 1706–1727, 2023

    2023

  4. [11]

    A survey on teleoperation,

    S. Lichiardopol, “A survey on teleoperation,” Technische Universiteit Eindhoven, Technical Report DCT-2007.155, 2007

    2007

  5. [12]

    Bilateral teleoperation: An histor- ical survey,

    P. F. Hokayem and M. W. Spong, “Bilateral teleoperation: An histor- ical survey,” Automatica, vol. 42, no. 12, pp. 2035–2057, 2006

    2006

  6. [13]

    Approaches to robotic teleoperation in a disaster scenario: From supervised autonomy to direct control,

    K. D. Katyal, C. Y . Brown, S. A. Hechtman, M. P. Para, T. G. McGee, K. C. Wolfe, R. J. Murphy, M. D. Kutzer, E. W. Tunstel, M. P. McLoughlin et al. , “Approaches to robotic teleoperation in a disaster scenario: From supervised autonomy to direct control,” in 2014 IEEE/RSJ Int...

    2014

  7. [14]

    Eye-hand behavior in human-robot shared manipulation,

    R. M. Aronson, T. Santini, T. C. Kübler, E. Kasneci, S. Srinivasa, and H. Admoni, “Eye-hand behavior in human-robot shared manipulation,” in Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 2018, pp. 4–13

    2018

  8. [15]

    Learning human- inspired force strategies for robotic assembly,

    S. Scherzinger, A. Roennau, and R. Dillmann, “Learning human- inspired force strategies for robotic assembly,” in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE). IEEE, 2023, pp. 1–8

    2023

  9. [16]

    Design and validation of two-handed multi- touch tabletop controllers for robot teleoperation,

    M. Micire, M. Desai, J. L. Drury, E. McCann, A. Norton, K. M. Tsui, and H. A. Yanco, “Design and validation of two-handed multi- touch tabletop controllers for robot teleoperation,” in Proceedings of the 16th international conference on Intelligent user interfaces , 2011, pp. 145–154

    2011

  10. [17]

    Bunny-visionpro: Real-time bimanual dexterous teleoperation for imitation learning,

    R. Ding, Y . Qin, J. Zhu, C. Jia, S. Yang, R. Yang, X. Qi, and X. Wang, “Bunny-visionpro: Real-time bimanual dexterous teleoperation for imitation learning,” arXiv preprint arXiv:2407.03162 , 2024

    2024 arXiv

  11. [18]

    Virtual reality teleoperation of a humanoid robot using markerless human upper body pose imitation,

    M. Hirschmanner, C. Tsiourti, T. Patten, and M. Vincze, “Virtual reality teleoperation of a humanoid robot using markerless human upper body pose imitation,” in 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) , 2019, pp. 259–265

    2019

  12. [19]

    Teleoperation of a humanoid robot using full-body motion capture, example movements, and machine learning,

    C. Stanton, A. Bogdanovych, and E. Ratanasena, “Teleoperation of a humanoid robot using full-body motion capture, example movements, and machine learning,” in Proc. Australasian Conference on Robotics and Automation, vol. 8, 2012, p. 51

    2012

  13. [20]

    Motion capture from inertial sensing for untethered humanoid teleoperation,

    N. Miller, O. C. Jenkins, M. Kallmann, and M. J. Mataric, “Motion capture from inertial sensing for untethered humanoid teleoperation,” in 4th IEEE/RAS International Conference on Humanoid Robots, 2004., vol. 2. IEEE, 2004, pp. 547–565

    2004

  14. [21]

    High-fidelity grasping in virtual reality using a glove-based system,

    H. Liu, Z. Zhang, X. Xie, Y . Zhu, Y . Liu, Y . Wang, and S.-C. Zhu, “High-fidelity grasping in virtual reality using a glove-based system,” in ICRA 2019. IEEE, 2019, pp. 5180–5186

    2019

  15. [22]

    Accelerating interactive human-like manipulation learning with gpu-based simulation and high-quality demonstrations,

    M. Mosbach, K. Moraw, and S. Behnke, “Accelerating interactive human-like manipulation learning with gpu-based simulation and high-quality demonstrations,” in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) . IEEE, 2022, pp. 435–441

    2022

  16. [23]

    Nim- bro avatar: Interactive immersive telepresence with force-feedback telemanipulation,

    M. Schwarz, C. Lenz, A. Rochow, M. Schreiber, and S. Behnke, “Nim- bro avatar: Interactive immersive telepresence with force-feedback telemanipulation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 5312–5319

    2021

  17. [24]

    Telesim: A modular and plug-and-play framework for robotic arm teleoperation using a digital twin,

    F. P. Audonnet, J. Grizou, A. Hamilton, and G. Aragon-Camarasa, “Telesim: A modular and plug-and-play framework for robotic arm teleoperation using a digital twin,” in ICRA 2024 . IEEE, 2024, pp. 17 770–17 777

    2024

  18. [25]

    Ace: A cross-platform visual-exoskeletons system for low-cost dexterous teleoperation,

    S. Yang, M. Liu, Y . Qin, R. Ding, J. Li, X. Cheng, R. Yang, S. Yi, and X. Wang, “Ace: A cross-platform visual-exoskeletons system for low-cost dexterous teleoperation,” arXiv preprint arXiv:2408.11805 , 2024

    2024 arXiv

  19. [26]

    Gello: A general, low- cost, and intuitive teleoperation framework for robot manipulators,

    P. Wu, Y . Shentu, Z. Yi, X. Lin, and P. Abbeel, “Gello: A general, low- cost, and intuitive teleoperation framework for robot manipulators,” 2023

    2023

  20. [27]

    Stability and transparency in bilateral teleoperation,

    D. A. Lawrence, “Stability and transparency in bilateral teleoperation,” IEEE transactions on robotics and automation , vol. 9, no. 5, pp. 624– 637, 1993

    1993

  21. [28]

    A sta- ble and transparent microscale force feedback teleoperation system,

    A. M. Ousaid, D. S. Haliyo, S. Régnier, and V . Hayward, “A sta- ble and transparent microscale force feedback teleoperation system,” IEEE/ASME Transactions on Mechatronics , vol. 20, no. 5, pp. 2593– 2603, 2015

    2015

  22. [29]

    Augmented reality for enhancing tele-robotic system with force feedback,

    Z. Zhao, P. Huang, Z. Lu, and Z. Liu, “Augmented reality for enhancing tele-robotic system with force feedback,” Robotics and Autonomous Systems, vol. 96, pp. 93–101, 2017

    2017

  23. [30]

    Implementation of augmented teleoperation system based on robot operating system (ros),

    D. Lee and Y . S. Park, “Implementation of augmented teleoperation system based on robot operating system (ros),” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 5497–5502

    2018

  24. [31]

    Telerobotic control using augmented reality,

    P. Milgram, A. Rastogi, and J. J. Grodski, “Telerobotic control using augmented reality,” in Proceedings 4th IEEE International Workshop on Robot and Human Communication . IEEE, 1995, pp. 21–29

    1995

  25. [33]

    Eve: En- abling anyone to train robots using augmented reality,

    J. Wang, C.-C. Chang, J. Duan, D. Fox, and R. Krishna, “Eve: En- abling anyone to train robots using augmented reality,” in Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology, 2024, pp. 1–13

    2024

  26. [34]

    Ar2-d2: Training a robot without a robot,

    J. Duan, Y . R. Wang, M. Shridhar, D. Fox, and R. Krishna, “Ar2-d2: Training a robot without a robot,” arXiv preprint arXiv:2306.13818 , 2023

    2023 arXiv

  27. [35]

    Apriltag: A robust and flexible visual fiducial system,

    E. Olson, “Apriltag: A robust and flexible visual fiducial system,” in 2011 IEEE international conference on robotics and automation . IEEE, 2011, pp. 3400–3407

    2011

  28. [36]

    Expressive body capture: 3d hands, face, and body from a single image,

    G. Pavlakos, V . Choutas, N. Ghorbani, T. Bolkart, A. A. Osman, D. Tzionas, and M. J. Black, “Expressive body capture: 3d hands, face, and body from a single image,” in CVPR, 2019, pp. 10 975– 10 985

    2019

  29. [37]

    Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning,

    K. Shaw, A. Agarwal, and D. Pathak, “Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning,” Robotics: Science and Systems (RSS), 2023

    2023

  30. [38]

    What matters in learning from offline human demonstrations for robot manipula- tion,

    A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y . Zhu, and R. Martín-Martín, “What matters in learning from offline human demonstrations for robot manipula- tion,” arXiv preprint arXiv:2108.03298 , 2021

    2021 arXiv

  31. [39]

    Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,

    T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel, “Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,” in ICRA 2018 . Ieee, 2018, pp. 5628–5635

    2018

  32. [40]

    Robust imitation learning from noisy demonstrations,

    V . Tangkaratt, N. Charoenphakdee, and M. Sugiyama, “Robust imitation learning from noisy demonstrations,” arXiv preprint arXiv:2010.10181, 2020

    2010 arXiv

  33. [41]

    Detecting incorrect visual demonstrations for improved policy learning,

    M. Hussein and M. Begum, “Detecting incorrect visual demonstrations for improved policy learning,” in Conference on Robot Learning . PMLR, 2023, pp. 1817–1827

    2023

  34. [42]

    Extraneousness- aware imitation learning,

    R. C. Zheng, K. Hu, Z. Yuan, B. Chen, and H. Xu, “Extraneousness- aware imitation learning,” in ICRA 2023. IEEE, 2023, pp. 2973–2979

    2023

  35. [43]

    How to train your robots? the impact of demonstration modality on imitation learning,

    H. Li, Y . Cui, and D. Sadigh, “How to train your robots? the impact of demonstration modality on imitation learning,” 2025. [Online]. Available: https://arxiv.org/abs/2503.07017 APPENDIX A. Technical Implementation of Pos-to-Pos Non-Collision Module Due to differences in acti...

    2025 arXiv

  36. [44]

    The primary objective of CPN is to predict the likelihood of self-collision for each link within a given joint configuration

    Self-Collision Prediction Network (CPN): To facilitate the transformation from invalid to valid configurations, we first develop a Self-Collision Prediction Network (CPN). The primary objective of CPN is to predict the likelihood of self-collision for each link within a given ...

  37. [45]

    The CCN takes an invalid configuration as input and outputs a corrected configuration that minimizes collision risks while closely resembling the original input

    Invalid Configuration Correction Network (CCN): Building on the CPN, we introduce an Invalid Configura- tion Correction Network (CCN) to map invalid configura- tions to valid ones. The CCN takes an invalid configuration as input and outputs a corrected configuration that minim...

  38. [46]

    • LCollision minimizes the likelihood of self-collision in the corrected configuration

    Explanation and Optimization Strategy: The loss terms in the proposed framework serve distinct roles: • LMSE ensures the corrected configuration retains conti- nuity with the original input. • LCollision minimizes the likelihood of self-collision in the corrected configuration...

  39. [47]

    This approach ensures the validity and continuity of robotic configurations, laying a robust foundation for subsequent task execution

    Summary: By integrating the CPN and CCN, we efficiently transform invalid self-collision configurations into valid ones. This approach ensures the validity and continuity of robotic configurations, laying a robust foundation for subsequent task execution. B. Bill of Materials ...

  40. [48]

    Each participant completed all tasks under both w/o Preview and w/ Preview conditions

    Subjective Workload Ratings: We asked participants to rate their experience using a 5-point Likert scale (higher values indicate greater intensity) across five dimensions: Mental Demand , Physical Demand , Total Demand , Frus- tration Level , and Perceived Performance. Each pa...

  41. [49]

    grasp” or “release

    Practice Time to Confidence: We also recorded the time participants spent practicing each control modality until they reported being comfortable with beginning formal task trials. Three input conditions were compared: our proposed method w/ Preview , the same setup w/o Preview...

Pith tools

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