Pith. sign in

REVIEW 3 major objections 6 minor 74 references

Grasping, relocation, in-hand rotation, and translation are the same task under different relational targets, so one policy can do all four and chain them.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.5

2026-07-31 14:54 UTC pith:NBL2XGIP

load-bearing objection Solid sim systems paper: shared relational interface plus DAgger really does chain grasp→relocate→in-hand motion across hands; the “intrinsic consistency” rhetoric runs a bit ahead of the skill-specific knobs. the 3 major comments →

arxiv 2607.28198 v1 pith:NBL2XGIP submitted 2026-07-30 cs.RO cs.CV

UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis

Hui Zhang , Julian Ferchow , Jie Song , Mirko Meboldt This is my paper
classification cs.RO cs.CV
keywords dexterous manipulationcross-skill policyhand-object interactionin-hand manipulationskill chainingreinforcement learningpolicy distillationmorphology transfer
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Dexterous work that keeps an object securely in the hand is usually split into four skills—grasp, move the hand-object pair, spin the object in the hand, and slide it in the hand—each trained with its own constraints, rewards, or even hand design. That split makes long sequences brittle because the ending state of one skill is often illegal for the next. This paper argues that all four are really the same problem: realize a desired hand–object relational motion while holding the object stable. With one shared state space, action space, and objective skeleton, ten specialist controllers distill into a single policy that matches specialists skill-by-skill, works on unseen shapes, tolerates pushes, runs on different hand designs, and chains grasp → relocate → in-hand motion without hand-offs. A sympathetic reader cares because that would turn skill libraries into one continuous controller instead of a patchwork of incompatible specialists.

Core claim

From a hand–object relational view, grasping, relocation, in-hand rotation, and in-hand translation are instantiations of one formulation that shares observation space, action space, and reward structure. Conditioning only the relational targets (what is tracked, fixed, or free) is enough to train per-skill experts and distill them with ordinary imitation into one cross-skill policy that stays strong on each skill, generalizes, resists disturbance, transfers across hand morphologies, and chains skills for long-horizon manipulation—evidence that the behaviors live in a shared solution space rather than incompatible skill silos.

What carries the argument

Hand–object relational formulation: object and wrist poses in hand and root frames, with each skill instantiated by marking targets Tracked, Fixed, or Free (Table 1), plus a shared interaction-aware object representation (contacts, forces, link-to-surface vectors) and a common goal/track/regularization reward skeleton; per-skill PPO experts then distill via DAgger into one policy.

Load-bearing premise

Marking relational targets as tracked, fixed, or free, together with one shared reward skeleton, is enough to make the four skills truly the same problem—even though contact and motion reward weights, training as separate experts first, and category-specific starting finger poses still differ by skill.

What would settle it

Train the same network capacity with skill-specific observation or action spaces (no shared relational targets or shared object representation), then test whether vanilla distillation still yields near-expert per-skill scores and whether continuous grasp→relocate→rotate/translate sequences keep high overall success without drops or illegal states at skill boundaries.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • A single controller can replace separate grasp, relocate, and in-hand specialists when the object must stay held.
  • Ending states of one skill remain valid starts for another, so long-horizon chaining needs no skill-specific resets or morphologies.
  • The same formulation transfers across hand designs (robot and human-like) without rewriting the task.
  • Interaction-centric shape features support geometry-adaptive finger strategies on shapes never seen in training.
  • Dexterous skills can be treated as structured variations of one task rather than isolated contact regimes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If relational targets are the real unifier, adding further held-object skills (e.g., controlled tilt or small regrasps) should plug into the same observation and reward skeleton with only new Tracked/Fixed/Free patterns.
  • Persistent skill-specific reward coefficients hint that full weight-sharing may still be unfinished; a follow-up with one coefficient set across all skills would test how much of the “shared formulation” is truly shared.
  • The same compatibility story, if it holds, would matter for any pipeline that must interleave stable hold with fine finger motion—tool use, assembly, or teleoperation priors—without mode-switching controllers.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper proposes UniCross, a simulation RL framework that casts grasping, relocation, in-hand rotation, and in-hand translation as instantiations of one hand–object relational formulation. All skills share observation layout (hand state, interaction-centric object features, mode one-hot, axis dh, multi-frame current/target poses), incremental joint actions with virtual wrist DOFs, and a reward skeleton r = r_goal + r_track + r_reg. Per-skill objectives are realized by Tracked/Fixed/Free assignments of target poses (Table 1, §3.2). Ten PPO experts are distilled with DAgger into one policy. Experiments (Allegro, MANO, Sharpa Wave) report strong per-skill success under a general wrist setting, generalization to unseen spheres/prisms, disturbance robustness, near-lossless distillation vs experts, and high success on grasp→relocate→rotate/translate sequences.

Significance. If the results hold, this is a concrete step toward generalist dexterous manipulation synthesis: a single controller that both masters canonical skills and chains them without skill-specific morphologies or palm-up assumptions. The Tracked/Fixed/Free interface is a clean, reusable task specification; cross-morphology transfer and long-horizon compatibility are practically valuable and uncommon in prior single-skill RL work. Strengths include systematic comparisons under original and general settings (Table 2), ablations of target-conditioned obs/act/reward and contact/distance features (Table 5), and explicit long-horizon metrics (Table 4). The contribution is primarily empirical systems work in simulation rather than a parameter-free theory, but the shared interface and demonstrated skill chaining are meaningful for graphics and robotics HOI synthesis.

major comments (3)
  1. [§3.1.4, Supp. Table 8; Abstract; §5] Abstract/§1/§5 claim a “common objective structure” and that skills are instantiations of a “shared solution space” revealing “intrinsic consistency.” The reward skeleton is shared (§3.1.4, Eq. 1–4), but Supp. Table 8 sets load-bearing coefficients skill-specifically (e.g. w_dis = 20 grasp vs 0.2 elsewhere; w_con/w_f differ by ~10×; w_p = 0 / 1 / 10 for grasp-relocate / rotate / translate). Training still uses ten separate PPO experts before DAgger (§3.3). Near-lossless distillation (Table 5) and long-horizon SR (Table 4) therefore show compatibility of carefully weighted experts under a shared interface, not that a single identical objective induces all four behaviors. Please either (i) report an ablation with fully shared coefficients, or (ii) temper Abstract/§5 language to “shared state/action spaces and a common reward template with skill-conditioned weights,” and discuss what is uni
  2. [Supp. §E; §4.1.1; Table 3] Grasping (and thus all downstream skills that start from grasp) is seeded from category-specific engineered finger spreads θ0/θ1 (Supp. §E), chosen so wrappable objects get multi-directional coverage and elongated objects get bilateral support for translation. §4.1.1 also trains only on boxes/cylinders. Table 3’s unseen-geometry results are encouraging, but the pipeline’s dependence on hand-crafted, category-conditioned initialization is not quantified. A control with a single generic open-hand (or randomized) init—or an explicit failure analysis when init is mismatched to object class—would show how much of the “unified” success is carried by this engineering versus the relational formulation.
  3. [Table 2; §4.2; Supp. §B] Baselines in Table 2 are adapted (GraspXL with top-down + table penalty; RotateIt/[63] teacher policies with axis rewards; a PD relocation baseline). Under Gen., rotation/translation baselines drop sharply, which the text attributes to palm-support assumptions. That comparison is informative but incomplete without reporting whether those baselines were given the same interaction-centric object features, virtual-wrist action space, and disturbance/domain randomization (Supp. §C), or only their original observation/action designs. Please state the observation/action parity explicitly and, if parity is incomplete, add a row where baselines receive the UniCross observation/action interface with their original rewards—so the gain is attributed to formulation rather than richer state or wrist freedom alone.
minor comments (6)
  1. [§3.1.2] §3.1.2: g_t is described as 55-dimensional; a short breakdown (10 one-hot + 3 dh + pose dims) would help readers verify the count and quaternion conventions (raw quat vs 6D).
  2. [Table 1; Eq. (3)] Table 1 “Free” targets are set to the current value so those DOFs are unconstrained—clarify whether the corresponding r_track weights are zeroed or the error is identically zero by construction (Eq. 3).
  3. [Fig. 2; §3.2] Rotation uses six directional experts and translation two (z± only). Justify omitting x/y translation and whether the unified policy can translate off-axis via the one-hot/dh interface at test time.
  4. [§4.1.2] Success thresholds (3 cm / 0.15 rad; π/2; 5 cm) and force ranges appear only in §4.1.2 / Supp.; a single evaluation-protocol table in the main text would aid reproducibility.
  5. [Fig. 3; Supp. Figs. 4–6] Fig. 3 and long-horizon figures in the supplement are helpful; ensure the main PDF marks which frames are MANO vs Allegro vs Sharpa when claiming morphology transfer in the caption.
  6. [§2.2; §3.3] Related work cites strong single-skill and HOI lines; a brief pointer to multi-task / goal-conditioned dexterous RL beyond the few works in §2.2 would situate the distillation choice (DAgger vs multi-task PPO from scratch).

Circularity Check

0 steps flagged

Empirical RL study with external success metrics; no prediction-by-construction or load-bearing circular chain.

full rationale

UniCross proposes a shared observation/action layout and reward skeleton for grasping, relocation, rotation, and translation, trains per-skill PPO experts, then distills them with DAgger. Claimed outcomes (per-skill success, unseen-object generalization, disturbance robustness, long-horizon chaining, morphology transfer) are measured against external task criteria—lift-without-drop, pose error thresholds, π/2 rotation, 5 cm translation—not algebraic restatements of fitted constants. Distillation imitates expert actions via MSE but does not define evaluation success by construction. Adoption of the GraspXL interaction-centric object representation is ordinary methodological reuse by overlapping authors; it is not invoked as a uniqueness theorem or as the sole justification that a multi-skill solution space exists. Skill-specific reward coefficients and engineered grasp initializations may weaken the rhetorical strength of “intrinsic consistency,” but that is an overclaim/correctness concern, not circularity: nothing in the derivation reduces Eq. X to Eq. Y by definition or renames a fit as a first-principles prediction. The paper is self-contained against its stated benchmarks.

Axiom & Free-Parameter Ledger

5 free parameters · 5 axioms · 1 invented entities

The work is engineering/empirical. Load-bearing commitments are simulation physics fidelity, the hand-centric contact/distance object encoding borrowed from prior grasping work, the Tracked/Fixed/Free relational goal template, PPO/DAgger optimization, and many hand-chosen reward scales and initial finger configurations. No new physical entities are postulated; free parameters are standard RL hyperparameters and skill-wise reward weights.

free parameters (5)
  • Skill-specific reward weights (w_dis, w_con, w_f, w_p, tracking and regularization coeffs) = See Supp. Table 8
    Supp. Table 8 sets different contact/distance/motion weights per skill (e.g. grasp w_dis=20 vs 0.2 elsewhere; translation w_p=10 vs rotation 1 vs grasp/relocate 0). Central 'common objective structure' claim depends on these hand-chosen scales.
  • Action scale α and PD controller gains
    Incremental action scaling and low-level PD conversion to torques shape explorability and smoothness; not derived, chosen for training stability (§3.1.3).
  • PPO/DAgger optimization hyperparameters = e.g. PPO lr 0.005; DAgger lr 3e-4, 3500 epochs
    Learning rates, horizons, batch sizes, episode lengths differ by skill family (Supp. Tables 6–7) and determine whether experts and the student converge.
  • Category-specific initial finger pose vectors θ0/θ1 = Explicit joint vectors in Supp. §E
    Supp. §E engineers different spread configurations for wrappable vs elongated objects to make later in-hand skills feasible; affects measured grasp and chaining success.
  • Success thresholds (3 cm / 0.15 rad relocate; π/2 rad; 5 cm translate; force magnitudes) = 3 cm, 0.15 rad, π/2, 5 cm; forces up to 10 m_obj g
    Evaluation binary success and disturbance ranges are chosen cutoffs that define reported SR numbers (§4.1–4.4).
axioms (5)
  • domain assumption Rigid-body contact physics in Isaac Gym sufficiently represents the hand-object skills being claimed.
    All training and evaluation are in Isaac Gym at 120 Hz sim / 20 Hz control (§3); no hardware validation.
  • domain assumption Hand-centric per-link distance, contact, and force features adequately encode object geometry for grasp and in-hand reconfiguration.
    Object representation s_o = [c,f,v] adopted from GraspXL and treated as cross-skill (§3.1.2); ablation in Table 5 supports usefulness but does not prove completeness.
  • ad hoc to paper The four canonical skills are fully specified by which hand/object poses are Tracked, Fixed, or Free in root vs hand frames.
    Core modeling choice in §3.1.1 and Table 1; excludes many real behaviors (regrasp from table, finger gaiting with intentional release, non-rigid objects, tools).
  • domain assumption Vanilla DAgger on states visited by the student with 100% expert query yields a fair single-network multi-skill policy of the same capacity.
    §3.3 distillation protocol; near-lossless match to experts in Table 5 is evidence under this assumption.
  • standard math Standard PPO with GAE is an adequate optimizer for the per-skill experts.
    Cited Schulman et al. PPO; routine in sim RL robotics.
invented entities (1)
  • UniCross unified relational skill formulation (shared obs/act/reward template + mode one-hot and d_h axis) no independent evidence
    purpose: Cast grasp/relocate/rotate/translate as instantiations of one goal-conditioned control problem for cross-skill distillation and chaining.
    Not a physical entity but a task formalism introduced by the paper; evidence is internal sim performance, not an external independent measurement.

pith-pipeline@v1.2.0-daily-grok45 · 23297 in / 3790 out tokens · 84332 ms · 2026-07-31T14:54:38.239905+00:00 · methodology

0 comments
read the original abstract

Many dexterous manipulation tasks require the object to remain securely held throughout the interaction. From the perspective of hand-object relational motion, such manipulation comprises four canonical skills: grasping, relocation, in-hand rotation, and in-hand translation. Human hands flexibly compose these skills to accomplish complex tasks. Existing approaches, however, model these skills separately with skill-specific action constraints, objectives, or even dedicated hand morphologies, which breaks the compatibility and continuity required for long-horizon composition. In this work, we present a unified framework that models all four skills in a single formulation that shares the same state and action spaces and a common objective structure. This formulation enables straightforward distillation of a single cross-skill policy that performs strongly on every skill, generalizes to unseen objects, stays robust to disturbances, and chains skills seamlessly into long-horizon manipulation. The framework also transfers effectively across different hand morphologies. Overall, our results suggest that different dexterous manipulation skills can be viewed as instantiations of a shared task formulation, revealing the intrinsic consistency across different behaviors.

Figures

Figures reproduced from arXiv: 2607.28198 by Hui Zhang, Jie Song, Julian Ferchow, Mirko Meboldt.

Figure 1
Figure 1. Figure 1: We present a unified dexterous manipulation framework that models grasping, relocation, in-hand translation, and in-hand [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed unified framework. Ten per-skill policies (one for grasp, one for relocate, six for rotation (x+/x-/y+/y- [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results on different hand morphologies and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Human Hand long-horizon manipulation Grasp + Relocate + Rotate Grasp + Relocate + Translate [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Allegro long-horizon manipulation Grasp + Relocate + Translate Grasp + Relocate + Rotate [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Sharpa Wave long-horizon manipulation sampled with f = 2.0 mobj ϵf , where ϵf ∼ N (0, I3) and mobj is the object mass. At each simulation step, the current perturbation force decays by 0.9, or is replaced by a new f with probability 0.25. D. Additional Qualitative Results We provide qualitative long-horizon manipulation results for the MANO, Allegro, and Sharpa Wave hands in Figs. 4 to 6. More qualitative … view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

74 extracted references · 3 linked inside Pith

  1. [1]

    Andrews and P.G

    S. Andrews and P.G. Kry. Goal directed multi-finger manip- ulation: Control policies and analysis.Computers & Graph- ics, 37(7):830–839, 2013. 2

    2013

  2. [2]

    Contactdb: Analyzing and predicting grasp contact via thermal imaging

    Samarth Brahmbhatt, Cusuh Ham, Charles C Kemp, and James Hays. Contactdb: Analyzing and predicting grasp contact via thermal imaging. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8709–8719, 2019. 2

    2019

  3. [3]

    Physically plausible full-body hand-object interaction synthesis

    Jona Braun, Sammy Christen, Muhammed Kocabas, Emre Aksan, and Otmar Hilliges. Physically plausible full-body hand-object interaction synthesis. In2024 International Conference on 3D Vision (3DV), pages 464–473. IEEE,

  4. [4]

    Text2hoi: Text-guided 3d motion generation for hand- object interaction

    Junuk Cha, Jihyeon Kim, Jae Shin Yoon, and Seungryul Baek. Text2hoi: Text-guided 3d motion generation for hand- object interaction. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 1577–1585, 2024. 2

    2024

  5. [5]

    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. InPro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9044–9053, 2021. 2

    2021

  6. [6]

    Dexonomy: Synthesizing all dexterous grasp types in a grasp taxonomy

    Jiayi Chen, Yubin Ke, Lin Peng, and He Wang. Dexonomy: Synthesizing all dexterous grasp types in a grasp taxonomy. Robotics: Science and Systems, 2025. 2

    2025

  7. [7]

    Karen Liu

    Sirui Chen, Albert Wu, and C. Karen Liu. Synthesizing dex- terous nonprehensile pregrasp for ungraspable objects. In ACM SIGGRAPH 2023 Conference Proceedings, New York, NY , USA, 2023. Association for Computing Machinery. 2

    2023

  8. [8]

    Springgrasp: Synthesizing compliant, dexterous grasps under shape uncer- tainty.Robotics: Science and Systems, 2024

    Sirui Chen, Jeannette Bohg, and C Karen Liu. Springgrasp: Synthesizing compliant, dexterous grasps under shape uncer- tainty.Robotics: Science and Systems, 2024. 2

    2024

  9. [9]

    Visual dexterity: In-hand reorientation of novel and complex object shapes.Science Robotics, 8(84):eadc9244, 2023

    Tao Chen, Megha Tippur, Siyang Wu, Vikash Kumar, Ed- ward Adelson, and Pulkit Agrawal. Visual dexterity: In-hand reorientation of novel and complex object shapes.Science Robotics, 8(84):eadc9244, 2023. 1, 3

    2023

  10. [10]

    D-grasp: Physi- cally plausible dynamic grasp synthesis for hand-object in- teractions

    Sammy Christen, Muhammed Kocabas, Emre Aksan, Jemin Hwangbo, Jie Song, and Otmar Hilliges. D-grasp: Physi- cally plausible dynamic grasp synthesis for hand-object in- teractions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 2

    2022

  11. [11]

    Diffh2o: Diffusion-based synthesis of hand-object interactions from textual descriptions

    Sammy Christen, Shreyas Hampali, Fadime Sener, Edoardo Remelli, Tomas Hodan, Eric Sauser, Shugao Ma, and Bugra Tekin. Diffh2o: Diffusion-based synthesis of hand-object interactions from textual descriptions. InSIGGRAPH Asia 2024 Conference Papers, pages 1–11, 2024. 3

    2024

  12. [12]

    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. InProceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pages 12943–12954, 2023. 2, 3

    2023

  13. [13]

    Imos: intent-driven full-body motion synthesis for human-object interactions

    Anindita Ghosh, Rishabh Dabral, Vladislav Golyanik, Chris- tian Theobalt, and Philipp Slusallek. Imos: intent-driven full-body motion synthesis for human-object interactions. In Computer Graphics Forum, pages 1–12. Wiley Online Li- brary, 2023. 2

    2023

  14. [14]

    FunGrasp: Functional grasping for diverse dex- terous hands.IEEE Robotics and Automation Letters, 2025

    Linyi Huang, Hui Zhang, Zijian Wu, Sammy Christen, and Jie Song. FunGrasp: Functional grasping for diverse dex- terous hands.IEEE Robotics and Automation Letters, 2025. 2

    2025

  15. [15]

    Hand-object contact consistency reasoning for hu- man grasps generation

    Hanwen Jiang, Shaowei Liu, Jiashun Wang, and Xiaolong Wang. Hand-object contact consistency reasoning for hu- man grasps generation. InProceedings of the International Conference on Computer Vision, 2021. 2

    2021

  16. [16]

    Superpadl: Scaling language-directed physics-based control with progressive supervised distillation

    Jordan Juravsky, Yunrong Guo, Sanja Fidler, and Xue Bin Peng. Superpadl: Scaling language-directed physics-based control with progressive supervised distillation. InSIG- GRAPH 2024 Conference Papers, 2024. 3

    2024

  17. [17]

    A grasp-based motion planning algorithm for character animation.The Journal of Visualization and Computer Animation, 12(3): 117–129, 2001

    Maciej Kalisiak and Michiel Van de Panne. A grasp-based motion planning algorithm for character animation.The Journal of Visualization and Computer Animation, 12(3): 117–129, 2001. 2

    2001

  18. [18]

    Grasp- ing field: Learning implicit representations for human grasps

    Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael J Black, Krikamol Muandet, and Siyu Tang. Grasp- ing field: Learning implicit representations for human grasps. In2020 International Conference on 3D Vision (3DV), pages 333–344. IEEE, 2020. 2

    2020

  19. [19]

    Karen Liu

    Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg, and C. Karen Liu. Simtoolreal: An object-centric policy for zero- shot dexterous tool manipulation, 2026. 3

    2026

  20. [20]

    Interaction capture and syn- thesis.ACM Transactions on Graphics (TOG), 25(3):872– 880, 2006

    Paul G Kry and Dinesh K Pai. Interaction capture and syn- thesis.ACM Transactions on Graphics (TOG), 25(3):872– 880, 2006. 2

    2006

  21. [21]

    Dex4d: Task-agnostic point track pol- icy for sim-to-real dexterous manipulation.arXiv preprint arXiv:2602.15828, 2026

    Yuxuan Kuang, Sungjae Park, Katerina Fragkiadaki, and Shubham Tulsiani. Dex4d: Task-agnostic point track pol- icy for sim-to-real dexterous manipulation.arXiv preprint arXiv:2602.15828, 2026. 3

    arXiv 2026

  22. [22]

    Maniptrans: Efficient dexterous bimanual manip- ulation transfer via residual learning

    Kailin Li, Puhao Li, Tengyu Liu, Yuyang Li, and Siyuan Huang. Maniptrans: Efficient dexterous bimanual manip- ulation transfer via residual learning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 2

    2025

  23. [23]

    Latenthoi: On the generalizable hand object motion generation with latent hand diffusion

    Muchen Li, Sammy Christen, Chengde Wan, Yujun Cai, Renjie Liao, Leonid Sigal, and Shugao Ma. Latenthoi: On the generalizable hand object motion generation with latent hand diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17416–17425, 2025. 3

    2025

  24. [24]

    Fu, and Nancy S

    Ying Li, Jiaxin L. Fu, and Nancy S. Pollard. Data-driven grasp synthesis using shape matching and task-based prun- ing.IEEE Transactions on Visualization and Computer Graphics, 13(4):732–747, 2007. 2

    2007

  25. [25]

    Karen Liu

    C. Karen Liu. Dextrous manipulation from a grasping pose. ACM Trans. Graph., 28(3), 2009. 2

    2009

  26. [26]

    Geneoh diffusion: Towards general- izable hand-object interaction denoising via denoising diffu- sion

    Xueyi Liu and Li Yi. Geneoh diffusion: Towards general- izable hand-object interaction denoising via denoising diffu- sion. InThe Twelfth International Conference on Learning Representations, 2024. 2 9

    2024

  27. [27]

    Dexndm: Closing the re- ality gap for dexterous in-hand rotation via joint-wise neural dynamics model, 2025

    Xueyi Liu, He Wang, and Li Yi. Dexndm: Closing the re- ality gap for dexterous in-hand rotation via joint-wise neural dynamics model, 2025. 3

    2025

  28. [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. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR), pages 21013–21022, 2022. 3

    2022

  29. [29]

    Taco: Benchmarking gener- alizable bimanual tool-action-object understanding

    Yun Liu, Haolin Yang, Xu Si, Ling Liu, Zipeng Li, Yuxiang Zhang, Yebin Liu, and Li Yi. Taco: Benchmarking gener- alizable bimanual tool-action-object understanding. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21740–21751, 2024. 2

    2024

  30. [30]

    Ratliff, and Karl Van Wyk

    Tyler Ga Wei Lum, Martin Matak, Viktor Makoviychuk, Ankur Handa, Arthur Allshire, Tucker Hermans, Nathan D. Ratliff, and Karl Van Wyk. DextrAH-g: Pixels-to-action dex- terous arm-hand grasping with geometric fabrics. InConfer- ence on Robot Learning (CoRL), 2024. 3

    2024

  31. [31]

    Omnigrasp: Grasping di- verse objects with simulated humanoids.Advances in Neural Information Processing Systems, 37:2161–2184, 2024

    Zhengyi Luo, Jinkun Cao, Sammy Christen, Alexander Win- kler, Kris Kitani, and Weipeng Xu. Omnigrasp: Grasping di- verse objects with simulated humanoids.Advances in Neural Information Processing Systems, 37:2161–2184, 2024. 2

    2024

  32. [32]

    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. 3

    2021

  33. [33]

    Contact-invariant optimization for hand manipulation

    Igor Mordatch, Zoran Popovi ´c, and Emanuel Todorov. Contact-invariant optimization for hand manipulation. In Proceedings of the ACM SIGGRAPH/Eurographics Sympo- sium on Computer Animation, pages 137–144, 2012. 2

    2012

  34. [34]

    To- kenhsi: Unified synthesis of physical human-scene inter- actions through task tokenization

    Liang Pan, Zeshi Yang, Zhiyang Dou, Wenjia Wang, Buzhen Huang, Bo Dai, Taku Komura, and Jingbo Wang. To- kenhsi: Unified synthesis of physical human-scene inter- actions through task tokenization. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 5379–5391, 2025. 3

    2025

  35. [35]

    Manivideo: Generating hand-object manipulation video with dexterous and generalizable grasping

    Youxin Pang, Ruizhi Shao, Jiajun Zhang, Hanzhang Tu, Yun Liu, Boyao Zhou, Hongwen Zhang, and Yebin Liu. Manivideo: Generating hand-object manipulation video with dexterous and generalizable grasping. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 2

    2025

  36. [36]

    Hoi-diff: Text-driven synthe- sis of 3d human-object interactions using diffusion models

    Xiaogang Peng, Yiming Xie, Zizhao Wu, Varun Jampani, Deqing Sun, and Huaizu Jiang. Hoi-diff: Text-driven synthe- sis of 3d human-object interactions using diffusion models. InProceedings of the Computer Vision and Pattern Recogni- tion Conference, pages 2878–2888, 2025. 2

    2025

  37. [37]

    Mcp: Learning composable hierarchical control with multiplicative compositional policies.Advances in neural information processing systems, 32, 2019

    Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, and Sergey Levine. Mcp: Learning composable hierarchical control with multiplicative compositional policies.Advances in neural information processing systems, 32, 2019. 3

    2019

  38. [38]

    Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters.ACM Trans

    Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler. Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters.ACM Trans. Graph., 41(4), 2022. 3

    2022

  39. [39]

    Pollard and Victor Brian Zordan

    Nancy S. Pollard and Victor Brian Zordan. Physically based grasping control from example. InProceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2005. 2

    2005

  40. [40]

    General In-Hand Object Rotation with Vision and Touch

    Haozhi Qi, Brent Yi, Sudharshan Suresh, Mike Lambeta, Yi Ma, Roberto Calandra, and Jitendra Malik. General In-Hand Object Rotation with Vision and Touch. InConference on Robot Learning (CoRL), 2023. 3, 6, 12

    2023

  41. [41]

    Learning complex dexterous manipulation with deep reinforcement learning and demonstrations

    Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giu- lia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine. Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. InProceedings of Robotics: Science and Systems (RSS), 2018. 2

    2018

  42. [42]

    Computer animation of knowledge-based human grasping.ACM Siggraph Computer Graphics, 25(4):339–348, 1991

    Hans Rijpkema and Michael Girard. Computer animation of knowledge-based human grasping.ACM Siggraph Computer Graphics, 25(4):339–348, 1991. 2

    1991

  43. [43]

    Allegro robot hand, 2013.https:// www.wonikrobotics.com/robot-hand

    Wonik Robotics. Allegro robot hand, 2013.https:// www.wonikrobotics.com/robot-hand. 6

    2013

  44. [44]

    Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together.ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 36(6), 2017. 7

    2017

  45. [45]

    A re- duction of imitation learning and structured prediction to no- regret online learning

    St ´ephane Ross, Geoffrey Gordon, and Drew Bagnell. A re- duction of imitation learning and structured prediction to no- regret online learning. InProceedings of the fourteenth inter- national conference on artificial intelligence and statistics, pages 627–635. JMLR Workshop and Conference Proceed- ings, 2011. 3, 5, 12

    2011

  46. [46]

    Proximal policy optimization algo- rithms.arXiv:1707.06347, 2017

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Rad- ford, and Oleg Klimov. Proximal policy optimization algo- rithms.arXiv:1707.06347, 2017. 3, 12

    Pith/arXiv arXiv 2017

  47. [47]

    Sharp wave, 2025.https://www.sharpa

    Sharpa. Sharp wave, 2025.https://www.sharpa. com/pages/wave. 7

    2025

  48. [48]

    Learning high-dof reaching-and-grasping via dynamic representation of gripper-object interaction.arXiv preprint arXiv:2204.13998, 2022

    Qijin She, Ruizhen Hu, Juzhan Xu, Min Liu, Kai Xu, and Hui Huang. Learning high-dof reaching-and-grasping via dynamic representation of gripper-object interaction.arXiv preprint arXiv:2204.13998, 2022. 3

    Pith/arXiv arXiv 2022

  49. [49]

    Two-stage diffusion for hands and artic- ulated objects interaction synthesis.Pattern Recognition,

    Wenqian Sun, Binghui Zuo, Zimeng Zhao, Zijing Wu, and Yangang Wang. Two-stage diffusion for hands and artic- ulated objects interaction synthesis.Pattern Recognition,

  50. [50]

    Grab: A dataset of whole-body human grasp- ing of objects

    Omid Taheri, Nima Ghorbani, Michael J Black, and Dim- itrios Tzionas. Grab: A dataset of whole-body human grasp- ing of objects. InEuropean conference on computer vision, pages 581–600. Springer, 2020. 2

    2020

  51. [51]

    Black, and Dim- itrios Tzionas

    Omid Taheri, Vasileios Choutas, Michael J. Black, and Dim- itrios Tzionas. GOAL: Generating 4D whole-body motion for hand-object grasping. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2

    2022

  52. [52]

    Omid Taheri, Yi Zhou, Dimitrios Tzionas, Yang Zhou, Duygu Ceylan, Soren Pirk, and Michael J. Black. GRIP: Generating interaction poses using latent consistency and spatial cues. InInternational Conference on 3D Vision (3DV), 2024. 2

    2024

  53. [53]

    Calm: Conditional adversar- ial latent models for directable virtual characters

    Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, and Xue Bin Peng. Calm: Conditional adversar- ial latent models for directable virtual characters. InACM 10 SIGGRAPH 2023 Conference Proceedings, New York, NY , USA, 2023. Association for Computing Machinery. 3

    2023

  54. [54]

    Maskedmimic: Unified physics-based char- acter control through masked motion inpainting.ACM Trans- actions on Graphics (TOG), 2024

    Chen Tessler, Yunrong Guo, Ofir Nabati, Gal Chechik, and Xue Bin Peng. Maskedmimic: Unified physics-based char- acter control through masked motion inpainting.ACM Trans- actions on Graphics (TOG), 2024. 3

    2024

  55. [55]

    Unidexgrasp++: Im- proving dexterous grasping policy learning via geometry- aware curriculum and iterative generalist-specialist learning

    Weikang Wan, Haoran Geng, Yun Liu, Zikang Shan, Yaodong Yang, Li Yi, and He Wang. Unidexgrasp++: Im- proving dexterous grasping policy learning via geometry- aware curriculum and iterative generalist-specialist learning. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 3891–3902, 2023. 3

    2023

  56. [56]

    Learning generalizable hand-object tracking from synthetic demonstrations.arXiv preprint arXiv:2512.19583, 2025

    Yinhuai Wang, Runyi Yu, Hok Wai Tsui, Xiaoyi Lin, Hui Zhang, Qihan Zhao, Ke Fan, Miao Li, Jie Song, Jingbo Wang, et al. Learning generalizable hand-object tracking from synthetic demonstrations.arXiv preprint arXiv:2512.19583, 2025. 2

    arXiv 2025

  57. [57]

    Saga: Stochastic whole- body grasping with contact

    Yan Wu, Jiahao Wang, Yan Zhang, Siwei Zhang, Otmar Hilliges, Fisher Yu, and Siyu Tang. Saga: Stochastic whole- body grasping with contact. InEuropean Conference on Computer Vision, pages 257–274. Springer, 2022

    2022

  58. [58]

    Synchronize dual hands for physics-based dexterous guitar playing

    Pei Xu and Ruocheng Wang. Synchronize dual hands for physics-based dexterous guitar playing. InSIGGRAPH Asia 2024 Conference Papers, New York, NY , USA, 2024. Asso- ciation for Computing Machinery. 2

    2024

  59. [59]

    Intermimic: Towards universal whole-body control for physics-based human-object interactions

    Sirui Xu, Hung Yu Ling, Yu-Xiong Wang, and Liangyan Gui. Intermimic: Towards universal whole-body control for physics-based human-object interactions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 3

    2025

  60. [60]

    CPF: Learning a contact potential field to model the hand-object interaction

    Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, and Cewu Lu. CPF: Learning a contact potential field to model the hand-object interaction. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 11097–11106, 2021. 7

    2021

  61. [61]

    Learning to use chopsticks in diverse gripping styles.ACM Transactions on Graphics (TOG), 41(4):1–17, 2022

    Zeshi Yang, Kangkang Yin, and Libin Liu. Learning to use chopsticks in diverse gripping styles.ACM Transactions on Graphics (TOG), 41(4):1–17, 2022. 3

    2022

  62. [62]

    Karen Liu

    Yuting Ye and C. Karen Liu. Synthesis of detailed hand ma- nipulations using contact sampling.ACM Transactions on Graphics (TOG), 31(4), 2012. 2

    2012

  63. [63]

    Learning in-hand translation using tactile skin with shear and normal force sensing

    Jessica Yin, Haozhi Qi, Jitendra Malik, James Pikul, Mark Yim, and Tess Hellebrekers. Learning in-hand translation using tactile skin with shear and normal force sensing. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA). IEEE, 2025. 1, 3, 6, 12

    2025

  64. [64]

    Dexteritygen: Foundation controller for unprecedented dexterity.arXiv preprint arXiv:2502.04307, 2025

    Zhao-Heng Yin, Changhao Wang, Luis Pineda, Francois Hogan, Krishna Bodduluri, Akash Sharma, Patrick Lan- caster, Ishita Prasad, Mrinal Kalakrishnan, Jitendra Malik, et al. Dexteritygen: Foundation controller for unprecedented dexterity.arXiv preprint arXiv:2502.04307, 2025. 3

    Pith/arXiv arXiv 2025

  65. [65]

    Oakink2: A dataset of bimanual hands-object manipulation in complex task completion

    Xinyu Zhan, Lixin Yang, Yifei Zhao, Kangrui Mao, Han- lin Xu, Zenan Lin, Kailin Li, and Cewu Lu. Oakink2: A dataset of bimanual hands-object manipulation in complex task completion. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 445–456, 2024. 2

    2024

  66. [66]

    ManipNet: Neural manipulation synthesis with a hand- object spatial representation.ACM Trans

    He Zhang, Yuting Ye, Takaaki Shiratori, and Taku Komura. ManipNet: Neural manipulation synthesis with a hand- object spatial representation.ACM Trans. Graph., 2021. 2

    2021

  67. [67]

    GraspXL: Generating grasping motions for di- verse objects at scale

    Hui Zhang, Sammy Christen, Zicong Fan, Otmar Hilliges, and Jie Song. GraspXL: Generating grasping motions for di- verse objects at scale. InEuropean Conference on Computer Vision, pages 386–403. Springer, 2024. 2, 3, 4, 6, 12

    2024

  68. [68]

    ArtiGrasp: Physically plausible synthesis of bi-manual dexterous grasp- ing and articulation

    Hui Zhang, Sammy Christen, Zicong Fan, Luocheng Zheng, Jemin Hwangbo, Jie Song, and Otmar Hilliges. ArtiGrasp: Physically plausible synthesis of bi-manual dexterous grasp- ing and articulation. In2024 International Conference on 3D Vision (3DV), pages 235–246. IEEE, 2024. 3

    2024

  69. [69]

    RobustDexGrasp: Robust dexterous grasping of general objects

    Hui Zhang, Zijian Wu, Linyi Huang, Sammy Christen, and Jie Song. RobustDexGrasp: Robust dexterous grasping of general objects. InConference on Robot Learning (CoRL),

  70. [70]

    Jiajun Zhang, Yuxiang Zhang, Liang An, Mengcheng Li, Hongwen Zhang, Zonghai Hu, and Yebin Liu. Manidext: Hand-object manipulation synthesis via continuous corre- spondence embeddings and residual-guided diffusion.IEEE Transactions on Pattern Analysis and Machine Intelligence,

  71. [71]

    Bimart: A unified ap- proach for the synthesis of 3d bimanual interaction with ar- ticulated objects

    Wanyue Zhang, Rishabh Dabral, Vladislav Golyanik, Vasileios Choutas, Eduardo Alvarado, Thabo Beeler, Marc Habermann, and Christian Theobalt. Bimart: A unified ap- proach for the synthesis of 3d bimanual interaction with ar- ticulated objects. InProc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2025. 2, 3

    2025

  72. [72]

    Robust realtime physics-based motion control for hu- man grasping.ACM Transactions on Graphics (TOG), 32 (6):1–12, 2013

    Wenping Zhao, Jianjie Zhang, Jianyuan Min, and Jinxiang Chai. Robust realtime physics-based motion control for hu- man grasping.ACM Transactions on Graphics (TOG), 32 (6):1–12, 2013

    2013

  73. [73]

    Cams: Canonicalized manipulation spaces for category-level functional hand-object manipulation synthe- sis

    Juntian Zheng, Qingyuan Zheng, Lixing Fang, Yun Liu, and Li Yi. Cams: Canonicalized manipulation spaces for category-level functional hand-object manipulation synthe- sis. InProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), 2023. 2, 3

    2023

  74. [74]

    Gears: Local geometry-aware hand-object interaction synthesis

    Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen, and Gerard Pons-Moll. Gears: Local geometry-aware hand-object interaction synthesis. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3 11 UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis Supplementary Material The supplementary material inc...

    2024