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REVIEW 4 major objections 5 minor 2 cited by

MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse Synthetic Data

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A mobile robot can learn to receive objects from people using only synthetic training data, beating fixed-base baselines by at least 15 percentage points in every test setting.

desk verdict A solid synthetic-data pipeline for mobile handover with a plausible central claim, but the sim-to-real bridge rests on a small, unquantified perception gap. read the letter →

arxiv 2501.04595 v2 pith:T7UX4SU2 submitted 2025-01-08 cs.RO

Zifan Wang , Ziqing Chen , Junyu Chen , Jilong Wang , Yuxin Yang , Yunze Liu , Xueyi Liu , He Wang
show 1 more author
Li Yi
This is my paper · ORCID
classification cs.RO
keywords human-robothandovermobilemanipulationsyntheticdatasim-to-realtransferimitationlearningpointcloudmotionsynthesisclosed-loopvisuomotorpolicy
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 claims that a mobile robot can learn to take objects from a person using only synthetic data, with no real-world demonstrations. The authors build a pipeline that generates over 100,000 diverse full-body human handover scenes, automatically plans safe, imitation-friendly robot trajectories, and distills them into a closed-loop policy that coordinates the robot's base and arm from segmented point clouds. They report at least 15 percentage points higher success than all baselines in every simulation setting, and real-world success rates of 80% (simple) and 63.3% (complex) in a user study. If correct, the result suggests the bottleneck for handover skills is not collecting real human-robot interaction data but scaling the right synthetic data and demonstration filtering.

What carries the argument

The paper's apparatus has three linked parts. The simulation environment is a two-phase human-motion synthesizer: a controllable motion-diffusion model produces diverse full-body pre-handover movement, a kinematic optimizer plus randomized handover pose sampling produces the arm motion that transfers the object, and an interactive trigger switches phases when the robot comes within reach, yielding over 100,000 scenes. Demonstration generation is a gradient-based trajectory optimizer with three custom losses: future obstacle avoidance over a foresee window, a final-pose constraint that stops the robot face-to-face with the person, and a vision-neural loss that penalizes states from which a learned pose predictor cannot recover the object pose, so the vision-to-action map stays learnable. The policy is a 4D imitation network: segmented point clouds of body, hand, and object from head and wrist cameras are enriched with flow computed by point-cloud registration between frames, encoded by set-abstraction layers with different sampling radii for body vs hand-object scales, and decoded by one MLP into simultaneous SE(2) base and SE(3) arm actions plus an auxiliary grasp-pose prediction.

What would settle it

Deploy the same synthetic-trained policy on real users whose handover style falls clearly outside the simulated distribution (e.g., handing over while walking backward, holding the object at hip height, or letting the torso occlude the hand) and compare success rates with the reported 80%/63%; a return to baseline-level success would show the claimed generality is bounded by the simulated motion distribution.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that a generalizable handover skill can be produced entirely in simulation: with no real demonstrations and no motion-capture assets, the pipeline generates diverse synthetic full-body human behavior, plans safe and imitation-friendly robot trajectories from oracle state knowledge, and distills them into a closed-loop visuomotor policy that moves base and arm together. The paper reports that this policy outperforms all baselines by at least 15 percentage points in simulation, and in a real-world user study succeeds in 80% of simple handovers and 63.3% of complex ones, versus 40% and 30% for a reproduced fixed-base baseline. It also reports that scaling demonstrations from 10k to 100k improves average success, while training on a small real motion-capture dataset substantially hurts performance, supporting the claim that synthetic scale and diversity are the active ingredient.

Load-bearing premise

The load-bearing premise is that point clouds segmented from real depth cameras are similar enough to the simulated point clouds used in training that the policy's behavior transfers; the paper itself notes that segmentation errors and depth-camera inaccuracies remain deployment issues.

Editorial extensions

If this is right

  • If the reported numbers hold, imitation-friendly demonstration generation is as important as the policy itself: removing the vision-neural loss reduces success by 11.6% on average.
  • Scaling demonstration count from 10k to 100k adds roughly 3.3% success on average, while cutting to 1k costs about 13.9%, so the framework's advantage is tied to cheap, large-scale synthetic generation.
  • Training on a small real motion-capture dataset instead of the large synthetic set drops average success by about 34.6%, suggesting synthetic diversity can outweigh real-data volume for this task.
  • The policy's use of full-body human point clouds, temporal flow, and simultaneous base-arm decoding each contributes 12-18 percentage points of success; removing any of them harms generalization.
  • Real-world transfer, if reproduced by others, would mean the same synthetic pipeline can produce deployable handover skills without any human-collected demonstrations.

Reading between the lines

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

  • The vision-neural loss is a general recipe: any demonstration generator whose states are differentiable could be steered toward easy vision-to-action learning, not just handover; this may transfer to other closed-loop visuomotor tasks.
  • Because the policy consumes egocentric segmented point clouds rather than robot-specific state, the same synthetic data pipeline could plausibly train a different mobile manipulator with no extra data collection, though the paper only evaluates one robot.
  • A direct test of the 'exclusively synthetic' claim is to remove the manual first-frame annotation used in real-world segmentation; the paper's deployment currently depends on that annotation, so an automatic-only version would probe how much of the transfer is due to the synthetic pipeline itself.
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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

4 major / 5 minor

Summary. The paper presents MobileH2R, a framework for learning vision-based human-to-mobile-robot handover entirely from synthetic data. It introduces three components: a pipeline that synthesizes diverse full-body human handover motions by combining a diffusion-based motion generator, LLM-generated prompts, and grasp/object asset libraries; an automatic demonstration generator built on a state-based planner with future obstacle avoidance, final-pose constraints, and a learned 'vision neural loss' intended to make demonstrations imitation-friendly; and a 4D imitation-learning policy that consumes segmented human-body, hand, and object point clouds and outputs coordinated base-arm actions. The method is evaluated in simulation across three test sets and in real-world user studies on a Galbot G1 robot, reporting at least +15% success-rate improvements over baselines in all compared settings.

Significance. If the claims hold, the paper would make a meaningful contribution: it is the first full-body mobile handover framework trained exclusively on synthetic demonstrations, and its simulation evaluation is unusually thorough, with consistent success rates across simple, complex, and mocap-derived test sets (Tables 1-4) and a detailed set of ablations isolating the contributions of data scale, human-motion diversity, safety losses, flow features, human-body input, and coordinated action decoding. The proposed demonstration-generation pipeline and the 4D imitation architecture are well motivated and the simulation evidence is internally coherent. The main weakness is the real-world validation, which is small and does not quantify the perception gap that is load-bearing for the 'exclusively synthetic data' claim. This is a fixable limitation, not a fundamental flaw.

major comments (4)
  1. [Section 4.5, Tables 5 and 9] The real-world evidence is too thin to support the headline transfer claim. The user study consists of 30 trials per condition across 5 users and 6 objects, with no confidence intervals, no per-user or per-object breakdown of variance, and no statistical significance test. The gap between 80% and 40% (simple) and 63.3% and 30% (complex) is large, but the clustered design means it could be driven by a small number of users or objects. Because the paper's central claim is that synthetic-only training transfers to the real world, the comparison should be accompanied by at least a paired significance test and ideally per-object/per-user success counts.
  2. [Section 4.5, Section C.1, Section D] The sim-to-real bridge relies on an unquantified match between real SAM2-based segmentation plus depth and the perfect segmentation used at training time, yet no perception diagnostic is reported. The policy is trained on clean point clouds (Section 3.3), while deployment uses SAM2 masks initialized manually on the first frame and real depth streams (Section C.1), and Section D concedes that 'occasional segmentation errors with SAM2 or inaccuracies in depth camera perception' are significant challenges. The manuscript should report per-trial segmentation quality or, failing that, a synthetic perturbation study that corrupts the training-time point clouds with realistic mask noise and depth noise to show the policy's robustness. Without this, the real-world success rates cannot separate the contribution of the learned policy from the contribution of favorable perception.
  3. [Section 3.2, Figure 3] The vision neural loss has a potential circularity risk that is not addressed. The estimator E is trained to predict the loss of the pose-prediction network P on rendered observations from the same simulation distribution used to generate demonstrations; E then guides the state-based trajectory optimization, and the resulting demonstrations train the final policy. The manuscript does not state whether E and P are evaluated on held-out states, nor does it show how well E's predicted loss correlates with P's actual loss on the optimized trajectories. If E merely selects states that are easy for the estimator rather than genuinely informative for the policy, the 'imitation-friendly' claim is not independently established. Please add a held-out correlation analysis between E's predictions and P's actual errors, and clarify the training/evaluation split for both networks.
  4. [Section A.2.2, Section A.2.3, Section 4 Dataset] Several load-bearing demonstration-generation hyperparameters are fixed empirically without sensitivity analysis, and one value is internally inconsistent. The main text (Section 4, Dataset) states that the human transitions to the handover phase when the human-robot distance is below 1m, while Appendix A.1 states a threshold of 0.5m. In addition, the foresee time (1.5s), replan interval (1.5s), and the grasping-pose ranking by distance from the human hand are presented as fixed choices with no ablation or sensitivity study. Since these parameters shape the entire demonstration distribution, at least a consistency fix and a brief empirical justification or sensitivity table are needed to support reproducibility.
minor comments (5)
  1. [Table 9 caption] The caption says the results are presented in 'Table 1 of the manuscript,' but the table being referenced is Table 5; this cross-reference should be corrected.
  2. [Tables 5 and 9] The baseline is labeled 'GenH2R(reprod.)' in Table 5 but 'GenH2R[49]' in Table 9; the two labels should be unified so readers know the same reproduction is being compared.
  3. [Section 3.2, page 5] There is a typo 'effe ctively' in the paragraph motivating the vision-state recovery estimator, and the sentence beginning 'If the vision input accurately recovers...' would be clearer if split into two sentences.
  4. [Section 4, Metrics] The definition of AS in Eq. (1) uses Success(t) as 'success rate considering only successful cases within t * Tmax,' but it is not immediately clear whether the integral is over success as a step function or over a smoothed rate; a one-line formal definition would remove ambiguity.
  5. [Section 4.1] The comparison with reinforcement learning methods is acknowledged as missing in Section D, but the main text does not mention this limitation when discussing baselines; adding a sentence there would make the evaluation scope clearer.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central sim-to-real claim is supported by held-out synthetic scenes, external mocap data, and real-robot trials, with no load-bearing reduction to fitted quantities or self-citations.

full rationale

The claimed chain is: (1) synthesize diverse full-body human motion using GMD, LLM prompts, ShapeNet/Acronym/DexGraspNet assets; (2) generate safe, imitation-friendly demonstrations with a CHOMP-based planner under obstacle, final-pose, and vision-neural losses; (3) distill these into a 4D policy with PointNet++ features and coordinated base-arm/SE(3) outputs; (4) evaluate on held-out scenes from the same generators ('m0','n0'), on real mocap data from DexYCB ('s0'), and on a real Galbot G1 robot with SAM2 segmentation. The vision neural loss is the only element that might look self-referential: E is a learned estimator of P's pose-recovery loss, trained on simulation renderings, and used as a differentiable surrogate to optimize demonstration trajectories. But this is an auxiliary data-generation heuristic; the paper's success metric is task success (secure grasp, no collision, no timeout), and the policy is not trained or scored with E or P. The eval is therefore not equal by construction to the demonstration-generation objective. Baselines are trained on the same demonstration data, and the real-world user study is an independent empirical check, with the SAM2/depth gap explicitly acknowledged in Section D as a deployment challenge rather than hidden in the metric. Self-citations to GenH2R are prior-work/baseline references, not uniqueness arguments that make the framework's choice forced. No circular step meets the quote-and-reduction bar.

Assumptions & free parameters 5 free parameters · 3 assumptions · 1 invented entities

The main free parameters are the demonstration generation hyperparameters (foresee time, replan interval, phase durations, transition distance). The paper fixes them by empirical testing without sensitivity analysis. The dependence on external datasets and models (AMASS, GMD, ShapeNet, Acronym, DexGraspNet, SAM2) is a domain assumption. The vision-state recovery estimator is an internal module with no independent evidence.

free parameters (5)
  • foresee_time = 1.5 s
    Foresee time for trajectory planning is fixed based on empirical testing (Appendix A.2.3). It affects the planner's predictive capability and the resulting demonstrations.
  • replan_interval = 1.5 s
    Replan interval is set as a tradeoff between trajectory quality and speed (Appendix A.2.2). No sensitivity analysis is provided.
  • handover_phase_duration = 1.05 s
    Duration of the arm-only handover motion phase, used to create demonstrations (Section 4). This choice affects the dynamics of the handover.
  • pre-handover_duration = 6 s
    Duration of the pre-handover full-body motion phase before the handover trigger (Section 4). It affects the diversity of human motion but is not central to the policy.
  • phase_transition_distance = 1 m (main), 0.5 m (appendix)
    The distance threshold for triggering the handover phase is stated as 1m in Section 4 and 0.5m in Appendix A.1. This inconsistency is a detail, but the threshold affects the interaction dynamics and demonstration distribution.
assumptions (3)
  • domain assumption GMD, trained on AMASS, is used as the motion prior for generating diverse full-body human motions.
    The realism and diversity of the synthetic human motion depends entirely on the quality and coverage of the external motion generation model (Section 3.1).
  • domain assumption The real-world policy can rely on SAM2 segmentation plus manual first-frame annotation to produce point clouds similar enough to the simulated training point clouds.
    This assumption is explicitly acknowledged in Section D of the appendix as a source of challenge.
  • domain assumption The physics simulation in PyBullet and the grasping poses from Acronym/DexGraspNet provide a sufficiently accurate model of real grasping and handover dynamics.
    The sim-to-real transfer depends on the fidelity of the simulation environment for object grasping and human-robot interaction (Section 3.2, Appendix B.1).
invented entities (1)
  • Vision-State Recovery Estimator E
    purpose: Estimates the vision neural loss for a given state so that trajectory optimization can be guided to produce imitation-friendly demonstrations.
    This is a learned module within the demonstration generation pipeline. It is used only to create demonstrations, and its effectiveness is measured indirectly through the final policy's success rate. It does not make a falsifiable prediction outside the paper.

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Cite this review

Pith. "Pith review of MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse Synthetic Data." pith.science (2026). https://pith.science/paper/T7UX4SU2

@misc{pith2026250104595,
  author       = {Pith},
  title        = {Pith review of: MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse Synthetic Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T7UX4SU2}},
  note         = {Machine review of arXiv:2501.04595}
}
read the original abstract

This paper introduces MobileH2R, a framework for learning generalizable vision-based human-to-mobile-robot (H2MR) handover skills. Unlike traditional fixed-base handovers, this task requires a mobile robot to reliably receive objects in a large workspace enabled by its mobility. Our key insight is that generalizable handover skills can be developed in simulators using high-quality synthetic data, without the need for real-world demonstrations. To achieve this, we propose a scalable pipeline for generating diverse synthetic full-body human motion data, an automated method for creating safe and imitation-friendly demonstrations, and an efficient 4D imitation learning method for distilling large-scale demonstrations into closed-loop policies with base-arm coordination. Experimental evaluations in both simulators and the real world show significant improvements (at least +15% success rate) over baseline methods in all cases. Experiments also validate that large-scale and diverse synthetic data greatly enhances robot learning, highlighting our scalable framework.

Figures

Figures reproduced from arXiv: 2501.04595 by the authors.

Figure 1
Figure 1. The overview of MobileH2R. We propose a framework for generalizable human-to-mobile-robot handover, including a scalable pipeline for diverse full-body human motion synthesis (a), an automatic method for producing safe, imitation-friendly demonstrations (b), an efficient 4D imitation learning approach to learn coordinated base-arm actions (c), and successful sim2real transfer (d). Abstract This paper introduces Mobi… view at source ↗
Figure 2
Figure 2. The overview of our framework. First, we propose an automatic pipeline to scale up synthetic and diverse full-body motion data for the handover task by integrating various synthetic digital asset libraries, generative models, and useful toolkits. Second, we introduce an automatic pipeline to scale up mobile robot demonstrations for safety and imitation-friendliness. Our approach aims to avoid collisions while enhanc… view at source ↗
Figure 3
Figure 3. Visualization for the vision neural loss. The Pose Prediction Network takes vision inputs and predicts the object pose. The prediction error is defined as the vision neural loss. The Vision-State Recovery Estimator takes states as input and es￾timates the vision neural loss, guiding the state-based trajectory optimization towards imitation-friendly demonstration generation. tion and how to generate imitation-friendl… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Qualitative results. We compare different methods in detail in the simulated scene and the real-world scene. Methods m0 n0 s0 w/o flow 58.0 47.2 53.5 w/o human 51.3 48.7 57.6 w/o coordinated action 51.0 41.7 48.9 Ours 63.8 53.4 77.8 [PITH_FULL_IMAGE:figures/full_fig_p…
Figure 5
Figure 5. Figure 5: Template prompt to LLMs to generate direct object￾aware motion description for controllable motion generator. C. Real World Experiments Details 17 C.1. Setup . . . . . . . . . . . . . . . . . . . . . 17 C.2. User Study . . . . . . . . . . . . . . . . . . 17 D. Limitati…
Figure 6
Figure 6. Figure 6: Combine the body motion and the hand-object pose. In our pipeline, the body motions are SMPL [29] parameters ob￾tained from GMD [23], and the hand-object poses are MANO [42] parameters obtained from DexGraspNet [47]. Here we combine these parameters with SMPL-X [38] mo…
Figure 7
Figure 7. Figure 7: Visualization for the arm kinematic optimizer. Given the final pose of the hand, the optimizer determines the optimal hand joint parameters by optimizing under joint constraints and handover task priors. Practically, we add proper randomization to generate diverse data…
Figure 9
Figure 9. Figure 9: Various objects for real-world handover. The image above displays various objects for mobile handover, including the can, the bowl, the bottles or some plastic objects. single NVIDIA GeForce RTX 4090 GPU. We also provide a supplementary video that showcases extensive r…

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Forward citations

Cited by 2 Pith papers

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

  1. DexH2R: A Benchmark for Dynamic Dexterous Grasping in Human-to-Robot Handover

    cs.RO 2025-06 conditional novelty 7.0 of 10

    DexH2R provides the first real-world, multi-view dataset for dynamic dexterous grasping in human-to-robot handover, together with a benchmark and a three-stage grasping method.

  2. A Survey: Learning Embodied Intelligence from Physical Simulators and World Models

    cs.RO 2025-07 conditional novelty 4.0 of 10

    Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.

Reference graph

Works this paper leans on

69 extracted references · 53 canonical work pages · cited by 2 Pith papers

  1. [1]

    Shapenet: An information-rich 3d model repository

    Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 4, 6

    arXiv 2015

  2. [2]

    Dexycb: A benchmark for capturing hand grasping of objects

    Yu-Wei Chao, Wei Yang, Yu Xiang, Pavlo Molchanov, Ankur Handa, Jonathan Tremblay, Yashraj S Narang, Karl Van Wyk, Umar Iqbal, Stan Birchfield, et al. Dexycb: A benchmark for capturing hand grasping of objects. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9044–9053, 2021. 1, 2, 6

    work page 2021

  3. [3]

    Handoversim: A simulation framework and benchmark for human-to-robot object han- dovers

    Yu-Wei Chao, Chris Paxton, Yu Xiang, Wei Yang, Bal- akumar Sundaralingam, Tao Chen, Adithyavairavan Murali, Maya Cakmak, and Dieter Fox. Handoversim: A simulation framework and benchmark for human-to-robot object han- dovers. In 2022 International Conference on Robotics and Automation (ICRA), pages 6941–6947. IEEE, 2022. 1, 2, 6

    work page 2022

  4. [4]

    Synh2r: Synthesizing hand- object motions for learning human-to-robot handovers.arXiv preprint arXiv:2311.05599, 2023

    Sammy Christen, Lan Feng, Wei Yang, Yu-Wei Chao, Ot- mar Hilliges, and Jie Song. Synh2r: Synthesizing hand- object motions for learning human-to-robot handovers.arXiv preprint arXiv:2311.05599, 2023. 2, 4

    arXiv 2023

  5. [5]

    Learning human-to-robot handovers from point clouds

    Sammy Christen, Wei Yang, Claudia P ´erez-D’Arpino, Ot- mar Hilliges, Dieter Fox, and Yu-Wei Chao. Learning human-to-robot handovers from point clouds. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9654–9664, 2023. 2, 5

    work page 2023

  6. [6]

    Nonlinear model pre- dictive control for human-robot handover with application to the aerial case

    Gianluca Corsini, Martin Jacquet, Hemjyoti Das, Amr Afifi, Daniel Sidobre, and Antonio Franchi. Nonlinear model pre- dictive control for human-robot handover with application to the aerial case. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7597–7604. IEEE, 2022. 2

    work page 2022

  7. [7]

    Pybullet, a python mod- ule for physics simulation for games, robotics and machine learning

    Erwin Coumans and Yunfei Bai. Pybullet, a python mod- ule for physics simulation for games, robotics and machine learning. http://pybullet.org, 2016–2021. 5

    2016

  8. [8]

    Imitating task and motion planning with visuomotor transformers

    Murtaza Dalal, Ajay Mandlekar, Caelan Garrett, Ankur Handa, Ruslan Salakhutdinov, and Dieter Fox. Imitating task and motion planning with visuomotor transformers. arXiv preprint arXiv:2305.16309, 2023. 3

    arXiv 2023

Show all 69 references
  1. [9]

    Objaverse: A universe of annotated 3d objects

    Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec...

    2023

  2. [10]

    Objaverse-xl: A universe of 10m+ 3d objects

    Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram V oleti, Samir Yitzhak Gadre, et al. Objaverse-xl: A universe of 10m+ 3d objects. Advances in Neural Informa- tion Processing Systems, 36, 2024. 2

    2024

  3. [11]

    Hu- man–robot object handover: Recent progress and future di- rection

    Haonan Duan, Yifan Yang, Daheng Li, and Peng Wang. Hu- man–robot object handover: Recent progress and future di- rection. Biomimetic Intelligence and Robotics, 4(1):100145,

  4. [12]

    Acronym: A large-scale grasp dataset based on simulation

    Clemens Eppner, Arsalan Mousavian, and Dieter Fox. Acronym: A large-scale grasp dataset based on simulation. In 2021 IEEE International Conference on Robotics and Au- tomation (ICRA), pages 6222–6227. IEEE, 2021. 4, 5, 6, 14

    2021

  5. [13]

    Arctic: A dataset for dexterous bimanual hand- object manipulation

    Zicong Fan, Omid Taheri, Dimitrios Tzionas, Muhammed Kocabas, Manuel Kaufmann, Michael J Black, and Otmar Hilliges. Arctic: A dataset for dexterous bimanual hand- object manipulation. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages...

    2023

  6. [14]

    Graspnet-1billion: A large-scale benchmark for general ob- ject grasping

    Hao-Shu Fang, Chenxi Wang, Minghao Gou, and Cewu Lu. Graspnet-1billion: A large-scale benchmark for general ob- ject grasping. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), pages 11444–11453, 2020. 6

    2020

  7. [15]

    Robust grasping across diverse sensor qualities: The graspnet-1billion dataset

    Hao-Shu Fang, Minghao Gou, Chenxi Wang, and Cewu Lu. Robust grasping across diverse sensor qualities: The graspnet-1billion dataset. The International Journal of Robotics Research, 2023. 6

    2023

  8. [16]

    Anygrasp: Robust and efficient grasp perception in spa- tial and temporal domains

    Hao-Shu Fang, Chenxi Wang, Hongjie Fang, Minghao Gou, Jirong Liu, Hengxu Yan, Wenhai Liu, Yichen Xie, and Cewu Lu. Anygrasp: Robust and efficient grasp perception in spa- tial and temporal domains. IEEE Transactions on Robotics,

  9. [17]

    Integrated task and motion planning

    Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tom´as Lozano-P´erez. Integrated task and motion planning. Annual review of control, robotics, and autonomous systems, 4:265–293, 2021. 3

    2021

  10. [18]

    Ego4d: Around the world in 3,000 hours of egocentric video

    Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al. Ego4d: Around the world in 3,000 hours of egocentric video. InPro- ceedings of the IEEE/CVF Conference on Computer Vision...

    2022

  11. [19]

    Scaling up and dis- tilling down: Language-guided robot skill acquisition

    Huy Ha, Pete Florence, and Shuran Song. Scaling up and dis- tilling down: Language-guided robot skill acquisition. arXiv preprint arXiv:2307.14535, 2023. 3

    2023 arXiv

  12. [20]

    Human-to-robot han- dover control of an autonomous mobile robot based on hand- masked object pose estimation

    Yu-Yun Huang and Kai-Tai Song. Human-to-robot han- dover control of an autonomous mobile robot based on hand- masked object pose estimation. IEEE Robotics and Automa- tion Letters, 9(9):7851–7858, 2024. 2

    2024

  13. [21]

    Human3.6m: Large scale datasets and predic- tive methods for 3d human sensing in natural environments

    Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. Human3.6m: Large scale datasets and predic- tive methods for 3d human sensing in natural environments. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 36(7):1325–1339, 2014. 3

    2014

  14. [22]

    Robot learning of mobile manipulation with reachability behavior priors

    Snehal Jauhri, Jan Peters, and Georgia Chalvatzaki. Robot learning of mobile manipulation with reachability behavior priors. IEEE Robotics and Automation Letters , 7(3):8399– 8406, 2022. 3 9

    2022

  15. [23]

    Guided motion diffusion for controllable human motion synthesis, 2023

    Korrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn, and Siyu Tang. Guided motion diffusion for controllable human motion synthesis, 2023. 2, 3, 12, 13

    2023

  16. [24]

    Hrl4in: Hierarchical reinforcement learning for interactive navigation with mobile manipulators

    Chengshu Li, Fei Xia, Roberto Martin-Martin, and Silvio Savarese. Hrl4in: Hierarchical reinforcement learning for interactive navigation with mobile manipulators. In Confer- ence on Robot Learning, pages 603–616. PMLR, 2020. 3

    2020

  17. [25]

    Deepim: Deep iterative matching for 6d pose estimation

    Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox. Deepim: Deep iterative matching for 6d pose estimation. In Proceedings of the European Conference on Computer Vi- sion (ECCV), pages 683–698, 2018. 14

    2018

  18. [26]

    Motion-x: A large- scale 3d expressive whole-body human motion dataset, 2024

    Jing Lin, Ailing Zeng, Shunlin Lu, Yuanhao Cai, Ruimao Zhang, Haoqian Wang, and Lei Zhang. Motion-x: A large- scale 3d expressive whole-body human motion dataset, 2024. 2, 3

    2024

  19. [27]

    Visual whole- body control for legged loco-manipulation

    Minghuan Liu, Zixuan Chen, Xuxin Cheng, Yandong Ji, Ri- Zhao Qiu, Ruihan Yang, and Xiaolong Wang. Visual whole- body control for legged loco-manipulation. arXiv preprint arXiv:2403.16967, 2024. 3

    2024 arXiv

  20. [28]

    Hoi4d: A 4d egocentric dataset for category-level human- object interaction

    Yunze Liu, Yun Liu, Che Jiang, Kangbo Lyu, Weikang Wan, Hao Shen, Boqiang Liang, Zhoujie Fu, He Wang, and Li Yi. Hoi4d: A 4d egocentric dataset for category-level human- object interaction. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition,...

    2022

  21. [29]

    Matthew Loper, Naureen Mahmood, Javier Romero, Ger- ard Pons-Moll, and Michael J. Black. SMPL: A skinned multi-person linear model. ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1–248:16, 2015. 13

    2015

  22. [30]

    Troje, Ger- ard Pons-Moll, and Michael J

    Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Ger- ard Pons-Moll, and Michael J. Black. AMASS: Archive of motion capture as surface shapes. In International Confer- ence on Computer Vision, pages 5442–5451, 2019. 2, 3

    2019

  23. [31]

    Isaac gym: High performance gpu-based physics sim- ulation for robot learning, 2021

    Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State. Isaac gym: High performance gpu-based physics sim- ulation for robot learning, 2021. 5

    2021

  24. [32]

    Guided imitation of task and motion planning

    Michael James McDonald and Dylan Hadfield-Menell. Guided imitation of task and motion planning. In Confer- ence on Robot Learning, pages 630–640. PMLR, 2022. 3

    2022

  25. [33]

    The role of robots in the service industry: Factors affecting human-robot inter- actions

    Emi Moriuchi and Samantha Murdy. The role of robots in the service industry: Factors affecting human-robot inter- actions. International Journal of Hospitality Management , 118:103682, 2024. 2

    2024

  26. [34]

    Towards robots that know when they need help: Affordance-based uncer- tainty for large language model planners

    James F Mullen Jr and Dinesh Manocha. Towards robots that know when they need help: Affordance-based uncer- tainty for large language model planners. arXiv preprint arXiv:2403.13198, 2024. 3

    2024 arXiv

  27. [35]

    Ai and human-robot interaction: A review of recent advances and challenges

    Alexander Obaigbena, Oluwaseun Augustine Lottu, Ejike David Ugwuanyi, Boma Sonimitiem Jacks, Enoch Oluwademilade Sodiya, and Obinna Donald Daraojimba. Ai and human-robot interaction: A review of recent advances and challenges. GSC Advanced Research and Reviews, 18(2):321–330, 2024. 2

    2024

  28. [36]

    Open x-embodiment: Robotic learning datasets and rt-x models

    Abby O’Neill, Abdul Rehman, Abhinav Gupta, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, et al. Open x-embodiment: Robotic learning datasets and rt-x models. arXiv preprint arXiv:2310.08864, 2023. 3

    arXiv

  29. [37]

    Gpt-4, 2023

    OpenAI. Gpt-4, 2023. Accessed: 2024-11-15. 4, 13

    2023

  30. [38]

    Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, and Michael J. Black. Expressive body capture: 3D hands, face, and body from a single image. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pa...

    2019

  31. [39]

    Pointnet++: Deep hierarchical feature learning on point sets in a metric space

    Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30, 2017. 5

    2017

  32. [40]

    Chomp: Gradient optimization tech- niques for efficient motion planning

    Nathan Ratliff, Matt Zucker, J Andrew Bagnell, and Sid- dhartha Srinivasa. Chomp: Gradient optimization tech- niques for efficient motion planning. In 2009 IEEE inter- national conference on robotics and automation, pages 489–

    2009

  33. [41]

    Sam 2: Segment anything in images and videos

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R¨adle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junt- ing Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Doll´ar, and Christoph Feic...

    2024 arXiv

  34. [42]

    Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bod- ies together. ACM Transactions on Graphics, (Proc. SIG- GRAPH Asia), 36(6), 2017. 13

    2017

  35. [43]

    Efficient variants of the icp algorithm

    Szymon Rusinkiewicz and Marc Levoy. Efficient variants of the icp algorithm. In Proceedings third international confer- ence on 3-D digital imaging and modeling , pages 145–152. IEEE, 2001. 15

    2001

  36. [44]

    Grasping in the wild: Learning 6dof closed- loop grasping from low-cost demonstrations

    Shuran Song, Andy Zeng, Johnny Lee, and Thomas Funkhouser. Grasping in the wild: Learning 6dof closed- loop grasping from low-cost demonstrations. IEEE Robotics and Automation Letters, 5(3):4978–4985, 2020. 3

    2020

  37. [45]

    Quadwbg: Generalizable quadrupedal whole-body grasping, 2024

    Jilong Wang, Javokhirbek Rajabov, Chaoyi Xu, Yiming Zheng, and He Wang. Quadwbg: Generalizable quadrupedal whole-body grasping, 2024. 3

    2024

  38. [46]

    Gensim: Generating robotic simulation tasks via large language models

    Lirui Wang, Yiyang Ling, Zhecheng Yuan, Mohit Shridhar, Chen Bao, Yuzhe Qin, Bailin Wang, Huazhe Xu, and Xiao- long Wang. Gensim: Generating robotic simulation tasks via large language models. arXiv preprint arXiv:2310.01361 ,

    arXiv

  39. [47]

    Dexgraspnet: A large-scale robotic dexterous grasp dataset for general ob- jects based on simulation, 2023

    Ruicheng Wang, Jialiang Zhang, Jiayi Chen, Yinzhen Xu, Puhao Li, Tengyu Liu, and He Wang. Dexgraspnet: A large-scale robotic dexterous grasp dataset for general ob- jects based on simulation, 2023. 4, 13

    2023

  40. [48]

    Robogen: Towards unleashing infi- nite data for automated robot learning via generative simula- tion

    Yufei Wang, Zhou Xian, Feng Chen, Tsun-Hsuan Wang, Yian Wang, Katerina Fragkiadaki, Zackory Erickson, David Held, and Chuang Gan. Robogen: Towards unleashing infi- nite data for automated robot learning via generative simula- tion. arXiv preprint arXiv:2311.01455, 2023. 3

    2023 arXiv

  41. [49]

    Genh2r: Learning generalizable human- to-robot handover via scalable simulation demonstration and 10 imitation

    Zifan Wang, Junyu Chen, Ziqing Chen, Pengwei Xie, Rui Chen, and Li Yi. Genh2r: Learning generalizable human- to-robot handover via scalable simulation demonstration and 10 imitation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , ...

    2024

  42. [50]

    Relmogen: Leverag- ing motion generation in reinforcement learning for mobile manipulation

    Fei Xia, Chengshu Li, Roberto Mart ´ın-Mart´ın, Or Litany, Alexander Toshev, and Silvio Savarese. Relmogen: Leverag- ing motion generation in reinforcement learning for mobile manipulation. arXiv preprint arXiv:2008.07792, 2020. 3

    2008 arXiv

  43. [51]

    Language models meet world models: Embodied experiences enhance lan- guage models

    Jiannan Xiang, Tianhua Tao, Yi Gu, Tianmin Shu, Zirui Wang, Zichao Yang, and Zhiting Hu. Language models meet world models: Embodied experiences enhance lan- guage models. Advances in neural information processing systems, 36, 2024. 3

    2024

  44. [52]

    Harmonic mobile manipulation

    Ruihan Yang, Yejin Kim, Rose Hendrix, Aniruddha Kemb- havi, Xiaolong Wang, and Kiana Ehsani. Harmonic mobile manipulation. arXiv preprint arXiv:2312.06639, 2023. 3

    2023 arXiv

  45. [53]

    Reactive human-to-robot handovers of arbitrary objects

    Wei Yang, Chris Paxton, Arsalan Mousavian, Yu-Wei Chao, Maya Cakmak, and Dieter Fox. Reactive human-to-robot handovers of arbitrary objects. In 2021 IEEE International Conference on Robotics and Automation (ICRA) , pages 3118–3124. IEEE, 2021. 2, 5

    2021

  46. [54]

    H2o: A benchmark for visual human-human object handover analysis

    Ruolin Ye, Wenqiang Xu, Zhendong Xue, Tutian Tang, Yan- feng Wang, and Cewu Lu. H2o: A benchmark for visual human-human object handover analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 15762–15771, 2021. 2

    2021

  47. [55]

    Asc: Adaptive skill coordination for robotic mobile manipulation

    Naoki Yokoyama, Alex Clegg, Joanne Truong, Eric Under- sander, Tsung-Yen Yang, Sergio Arnaud, Sehoon Ha, Dhruv Batra, and Akshara Rai. Asc: Adaptive skill coordination for robotic mobile manipulation. IEEE Robotics and Automa- tion Letters, 9(1):779–786, 2023. 3

    2023

  48. [56]

    Flexible handover with real-time robust dynamic grasp tra- jectory generation

    Gu Zhang, Hao-Shu Fang, Hongjie Fang, and Cewu Lu. Flexible handover with real-time robust dynamic grasp tra- jectory generation. arXiv preprint arXiv:2308.15622, 2023. 2 11 MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse ...

    2023 arXiv

  49. [57]

    Human-to-Robot Handovers

    Related Work 2 2.1. Human-to-Robot Handovers . . . . . . . . . 2 2.2. Mobile Robot Manipulation . . . . . . . . . 3 2.3. Scaling up Demonstrations for Imitation . . . 3

  50. [58]

    MobileH2R-Sim

    Method 3 3.1. MobileH2R-Sim . . . . . . . . . . . . . . . 3 3.2. Safe and Imitation-friendly Demonstration . 4 3.3. Imitation for Coordinated Base-Arm Actions 5

  51. [59]

    Evaluation on Different Methods

    Experiments 6 4.1. Evaluation on Different Methods . . . . . . . 6 4.2. Evaluation on Data Scaling . . . . . . . . . . 7 4.3. Evaluation on Demonstration Strategies . . . 7 4.4. Ablations . . . . . . . . . . . . . . . . . . . 7 4.5. Real World Experiments . . . . . . . . . . . 8

  52. [60]

    as if",

    Conclusion 8 Appendix 12 A . More Method Details 12 A.1 . MobileH2R-Sim . . . . . . . . . . . . . . . 12 A.2 . Demonstration Workflow . . . . . . . . . . . 14 A.3 . Imitation for coordinated Based-Arm Actions 14 B . Simulation Experiments Details 15 B.1. Training Details . . ....

  53. [61]

    The trajectory buffer is empty

  54. [62]

    The replan interval elapses

  55. [63]

    s0” results in a significant average drop in the success rate by 34.6%. When trained on the simpler human- involved “m0

    Reactive behavior occurs (e.g., transitioning from the pre-handover phase to the handover phase). If replanning is required, proceed to Step 3; otherwise, execute the current trajectory in Step 4. In our experiments, we set the replan interval to 1.5 seconds. This interval rep...

  56. [64]

    m0”. In the complex setting, users may sit, go downstairs, or perform adversarial actions to mimic “n0

    to obtain segmentation masks in our real-world exper- iments. Specifically, we manually annotate the object, the human hand, and the human body in the first RGB image using our control panel as shown in Figure 8, and then leverage the tracking capability of the SAM2 model to g...

  57. [65]

    mini cocoa crisps 2 / 5 5 / 5 2 / 5 3 / 5

  58. [66]

    adhesive tape 3 / 5 4 / 5 2 / 5 3 / 5 3.chewing gum container 1 / 5 3 / 5 1 / 5 3 / 5

  59. [67]

    instant noodles 1 / 5 4 / 5 1 / 5 4 / 5

  60. [68]

    chicken jerky 3 / 5 4 / 5 1 / 5 4 / 5

  61. [69]

    User study for sim-to-real experiments

    bottled iced tea 2 / 5 4 / 5 2 / 5 2 / 5 total 12 / 30 (40%) 24 / 30 (80%) 9 / 30 (30%) 19 / 30 (63%) Table 9. User study for sim-to-real experiments. our method and GenH2R(reprod.) method were evaluated by five individuals for six objects in both the simple and complex settin...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.