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DemoGrasp: Universal Dexterous Grasping from a Single Demonstration

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
dexterous graspingreinforcement learningsim-to-real
Abstract:

Universal grasping with multi-fingered dexterous hands is a fundamental challenge in robotic manipulation. While recent approaches successfully learn closed-loop grasping policies using reinforcement learning (RL), the inherent difficulty of high-dimensional, long-horizon exploration necessitates complex reward and curriculum design, often resulting in suboptimal solutions across diverse objects. We propose DemoGrasp, a simple yet effective method for learning universal dexterous grasping. We start from a single successful demonstration trajectory of grasping a specific object and adapt to novel objects and poses by editing the robot actions in this trajectory: changing the wrist pose determines where to grasp, and changing the hand joint angles determines how to grasp. We formulate this trajectory editing as a single-step Markov Decision Process (MDP) and use RL to optimize a universal policy across hundreds of objects in parallel in simulation, with a simple reward consisting of a binary success term and a robot–table collision penalty. In simulation, DemoGrasp achieves a 95% success rate on DexGraspNet objects using the Shadow Hand, outperforming previous state-of-the-art methods. It also shows strong transferability, achieving an average success rate of 84.6% across diverse dexterous hand embodiments on six unseen object datasets, while being trained on only 175 objects. Through vision-based imitation learning, our policy successfully grasps 110 unseen real-world objects, including small, thin items. It generalizes to spatial, background, and lighting changes, supports both RGB and depth inputs, and extends to language-guided grasping in cluttered scenes.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

DemoGrasp proposes learning universal dexterous grasping by editing a single demonstration trajectory—adjusting wrist pose for 'where' and joint angles for 'how'—then optimizing via RL across hundreds of objects. The paper resides in the Cross-Embodiment and Generalization Methods leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader taxonomy. This leaf sits under Learning-Based Grasp Synthesis and Control, distinguishing itself from single-embodiment RL approaches and pure imitation learning branches by emphasizing transferability across diverse hand morphologies and object categories.

The taxonomy reveals neighboring leaves focused on Reinforcement Learning for Grasping (including Deep RL for Dexterous Manipulation and RL with Bionic Reflexes) and Human-Inspired Learning Approaches (covering Imitation Learning from Human Demonstrations and RL with Human Pose Priors). DemoGrasp bridges these areas: it starts from a human-like demonstration but formulates trajectory editing as a single-step MDP optimized via RL, rather than pure imitation or multi-step RL exploration. Nearby branches like Model-Based Grasp Planning and Specialized Grasping Tasks address complementary challenges—contact mechanics and task-specific scenarios—but do not emphasize the demonstration-editing paradigm central to this work.

Among 19 candidates examined, the DemoGrasp framework contribution shows 2 refutable candidates out of 8 examined, suggesting some prior work on demonstration-driven universal grasping exists within this limited search scope. The single-step MDP formulation encountered 1 refutable candidate from 1 examined, indicating at least one overlapping prior approach to trajectory editing or simplified action spaces. The vision-based sim-to-real transfer contribution found 0 refutable candidates among 10 examined, appearing more novel within the sampled literature. These statistics reflect a top-K semantic search plus citation expansion, not an exhaustive survey, so additional relevant work may exist beyond the 19 papers analyzed.

Overall, the paper occupies a sparsely populated taxonomy leaf and introduces a demonstration-editing perspective that differs from mainstream multi-step RL or pure imitation paradigms. The limited search scope—19 candidates across three contributions—provides useful signals but cannot definitively rule out related work in adjacent research communities or recent preprints. The framework's novelty appears strongest in its sim-to-real transfer component, while the core demonstration-editing concept shows some overlap with prior efforts identified in the analysis.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
19
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: universal dexterous grasping with multi-fingered robotic hands. The field organizes itself around several complementary perspectives. Learning-Based Grasp Synthesis and Control encompasses data-driven approaches that leverage reinforcement learning, imitation learning, and neural network architectures to acquire grasping policies, often emphasizing cross-embodiment transfer and generalization across diverse hand morphologies. Model-Based Grasp Planning and Control focuses on analytical methods grounded in contact mechanics, optimization, and classical control theory. Specialized Grasping Tasks and Scenarios address domain-specific challenges such as deformable objects, multi-object manipulation, and functional grasping requirements. Hand Design and Embodiment explores the mechanical and actuation principles underlying anthropomorphic and underactuated hands, while Sensing and Perception for Grasping investigates tactile, visual, and multimodal feedback integration. Theoretical Foundations and Human Grasping Analysis draw insights from biomechanics and neuroscience, and a smaller cluster examines Precision and Power Grasp Transitions. Reviews and Surveys provide periodic snapshots of progress across these branches. Within the learning-based domain, a particularly active line of work targets cross-embodiment generalization—enabling policies trained on one hand design to transfer to others with minimal retraining. DemoGrasp[0] situates itself squarely in this cluster, proposing demonstration-driven methods that bridge morphological differences. Nearby efforts such as CEDex[25] and D r o Grasp[37] similarly emphasize transferability and data efficiency, though they may differ in whether they rely on large-scale simulation, real-world human demonstrations, or hybrid strategies. A recurring tension across these works involves balancing the richness of hand-specific priors against the flexibility needed for universal applicability. Meanwhile, other branches like Gamma[3] and Grasp Multiple Objects[4] explore task-level generalization rather than embodiment transfer, highlighting the field's dual focus on hardware diversity and task complexity. DemoGrasp[0] thus represents an effort to leverage human expertise for cross-platform dexterity, contrasting with purely simulation-driven or model-based alternatives.

Claimed Contributions

DemoGrasp framework for universal dexterous grasping via demonstration editing

The authors introduce DemoGrasp, a framework that learns universal dexterous grasping policies by editing a single demonstration trajectory. The method changes wrist poses to determine where to grasp and hand joint angles to determine how to grasp, formulating trajectory editing as a single-step MDP optimized via RL with a simple reward combining binary success and collision penalty.

8 retrieved papers
Can Refute
Single-step MDP formulation with demonstration-editing action space

The authors reformulate the grasping task as a single-step MDP where the policy outputs editing parameters (end-effector transformation and delta hand joint angles) that modify a demonstration trajectory. This compact action space and short horizon significantly reduce exploration challenges and eliminate the need for complex reward shaping used in prior methods.

1 retrieved paper
Can Refute
Vision-based sim-to-real transfer via flow-matching imitation learning

The authors develop a sim-to-real transfer approach by training a flow-matching policy on successful rollouts from the learned RL policy with rendered camera images in simulation. This enables zero-shot deployment on real robots with various camera configurations (RGB and depth) and demonstrates strong generalization to spatial, background, and lighting changes.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DemoGrasp framework for universal dexterous grasping via demonstration editing

The authors introduce DemoGrasp, a framework that learns universal dexterous grasping policies by editing a single demonstration trajectory. The method changes wrist poses to determine where to grasp and hand joint angles to determine how to grasp, formulating trajectory editing as a single-step MDP optimized via RL with a simple reward combining binary success and collision penalty.

Contribution

Single-step MDP formulation with demonstration-editing action space

The authors reformulate the grasping task as a single-step MDP where the policy outputs editing parameters (end-effector transformation and delta hand joint angles) that modify a demonstration trajectory. This compact action space and short horizon significantly reduce exploration challenges and eliminate the need for complex reward shaping used in prior methods.

Contribution

Vision-based sim-to-real transfer via flow-matching imitation learning

The authors develop a sim-to-real transfer approach by training a flow-matching policy on successful rollouts from the learned RL policy with rendered camera images in simulation. This enables zero-shot deployment on real robots with various camera configurations (RGB and depth) and demonstrates strong generalization to spatial, background, and lighting changes.

Table of Contents

DemoGrasp: Universal Dexterous Grasping from a Single Demonstration | Novelty Validation