DemoGrasp: Universal Dexterous Grasping from a Single Demonstration
Authors: Haoqi Yuan
Ziye Huang
Ye Wang
Chuan Mao
Chaoyi Xu
Zongqing Lu
Venue: ICLR 2026 Poster (2026)
Keywords: dexterous grasping
reinforcement learning
sim-to-real
Primary area: applications to robotics, autonomy, planning

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.

Published as a conference paper at ICLR 2026
DEMOGRASP: UNIVERSAL DEXTEROUS GRASPING FROM A SINGLE DEMONSTRATION
Haoqi Yuan 1.2- Ziye Huang! Ye Wang
Chuan Mao'
Chaoyi Xu²
Zongqing Lu
School of Computer Science, Peking University BBeingBeyond
Renmin University of China
Allegro
Inspbe
Schunk
DOser
Single
DemoGrasp
87% Success Rate on 110 Real-World Objects
Extend to Vanlous Embodiments and Danosets
Tenn de trind
Grosp Small & This Objects
Language Guided Cluttere Group
Arico
501A Performance on DesGraspNet with Shadow Hand
Universal Grosping
Variaus Cameraa Senvons
Lighting Genevoiiaaiio
Figure 1: DemoGrasp is a framework for learning universal dexterous grasping policies via reinforce- ment learning (RL) augmented with a single demonstration. It achieves state-of-the-art performance across diverse robotic hand embodiments and transfers effectively to real robots, demonstrating strong generalization.
ABSTRACT
Universal grasping with multi-fingered dexterous hands is 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 cur- riculum 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 single successful demonstration of grasping specific object and adapt to novel objects and poses by editing the robot actions in this demonstration: changing the wrist pose determines where grasp, and changing the hand joint angles determines how to grasp. We formulate this trajectory editing single-step Markov Decision Process (MDP) and use RL to optimize univer- sal policy across hundreds of objects in parallel in simulation, with simple reward combining binary success and robot-tabe table collision penalty. To enable world deployment, we collect rollouts using the trained RL policy with rendered images in simulation and apply imitation learning to obtain a closed-loop vision-based policy. In simulation, Demo Grasp achieves a 95% success rate on DexGrasppNa objects using the Shadow Hand, outperforming previous state-of-the-art methods. It also shows strong transferability, achieving average success rate of 84.6% across diverse dexterous hand embodiments on six unseen object datasets, while being trained only 175 objects. In real-world tests, our dision-based policy
"Equal contribution.
Correspondenc:: zonggi ng. 10@kku edu.en

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Published as a conference paper at ICLR 2026
successfully grasps 110 unseen objects, including small, thin items. It generalizes to spatial, background, and lighting changes, supports both RGB and depth inputs, and extends to manguag--guider grasping in cluttered scenes. Videos are available on our project page: https://eeeerccb...eeeeeeeooooed
INTRODUCTION
Universal dexterous grasping (Bicchi, 2000, Duan et al., 2021) a fundamental capability for real- world robots. The anthropomorphic design of dexterous robotic hands makes them the most suitable manipulators for real-world manipulation tasks, such as tool use, in-hand reorientation, and bimanual coordination. Universal grasping is therefore an essential prerequisite for enabling these sophisticated interactions. Though basic in concept, learning universal dexterous grasping policies remains far from simple. The high-dimensional action space introduced by dexterous hands with many degrees of freedom (DoFs), together with the long-horizon nature of closed-loop grapping. imposes substantial exploration challenges for reinforcement learning (RL). At the same time, the diverse geometries of objects make universal dexterous grasping a multi-task optimization problem, introducing additional difficulties such as catastrophic forgetting (Kirkpatrick et al. 2017: Schwarz et al. 2018) and gradient interference (Teh et al., 2017: Yu et al. 2020).
Recent studies have extensively investigated the use of RL for training universal dexterous grasping policies. Xu et al. (2023); Wan et al. (2023); Zhang et al. (2025b); Chen et al. (2025b) introduce techniques in observation feature design, dense reward shaping, and curriculum learning strategies to facilitate policy learning. UniDexGraspt++ (Wan et al.] (023) employs an iterative distillation process to improve teacher-student learning. ResDex (Huang et al. 2025) introduces a two-stage residual RL framework accelerate multi-task exploration. UniGrasp Transformer (Wang et al. 2025) proposes exhaustive RL on individual objects and distillation with expressive Transformer policies to bypass multi-task RL. However, many of these approaches train on hands without robot arms (Xu et al.
Wan et al. 2023). use privileged contact information as observations (Wan et al.) 2023 Huang et al. 2025). and face trade-off between collision penalties and other complex reward terms (Xu
2023
ct al.
2023 Huang et al. 22055), limiting their potential for deployment on real robots. Singh et al.
(2024);
Zhang et al. (2025b) achieve sim-to-real on wide variety of objects but still fall short on
grasping small, thin objects in tabletop settings. In addition, their reliance on complicated observation design, reward shaping, and multi stage pipelines increases the barrier to extending these methods to new embodiments and task settings.
In this research, we propose DemoGrasp, a simple yet powerful framework for universal dexterous grasping that addresses these challenges. Our key insight is that a single demonstration trajectory of grasping specific object encodes many transferable patterns for universal grasping, such as approaching the object's grasp center, squeezing the hand pose, and lifting the wrist. To grasp various objects different poses, we can slightly modify the robot actions within this trajectory and replay the edited actions. For example, to grasp the same object at a different location, we apply transformation to the wrist poses in the trajectory, changing where to grasp; to grasp a larger object at the same position, we adjust the grasp poses be more open, changing how to grasp. In method, the RL. policy explores how to edit the demonstration along these two axes, rather than exploring in the low-level robot action space as in prior methods (Xu et al. 2023 Wang et al. 2025 2025b), resulting in more efficient trial-and-error.
Zhang et al.)
Specifically, we formulate the demonstratioonecdiingg task as a single-step Markov Decision Process (MDP). At each trial, given an arbitrary object placed at a random position, the policy outputs an SE(3) transformation and delta hand joint angles, which are used to modify the end-effector poses and hand actions in the demonstration. The edited demonstration is then replayed in simulation, yielding a reward for the whole episode. By restricting the policy to a compact action space and a single-step decision-making horizon, the multi-task exploration burden is significantly reduced, removing the need for complex reward shaping. This enables us to effectively train a universal grasping policy on hundreds or thousands of objects by optimizing a simple combination of binary success reward and collision penalty. We observe that this design yields both superior performance in simulation and easy sim-to-real transfer with minimal collisions. We train a flow-mate now-mngc (Lipman al. 2022) policy on successful rollouts of the learned policy with rendered camera images in simulation, enabling zero-shot deployment on a real robot. Figure provides overview of our approach.
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Approach
Grasp
Demo Editing
Sim-to-Real
Demo
Transformaion
End-tHecttr
Initial
Tranaformation T
Object Inttll
2%
Demo Editor
Hand Pose Inter polotion
Rendered Trajectories
Full Object
Graiping Pose
Ap
RoB
Images
Reward Function
Proprio.
Flow Matching
MR
binary
collision penalty
Motion Planning
Replay the Edited Demo
Figure 2: DemoGrasp uses single demonstration trajectory to learn universal dexterous grasping, formulating each grasping trial as demonstration--ciiing process. For each trial, the Demo Editor policy takes observations the first timestep and outputs end-effector transformation and delta hand pose. The actions the demonstration are then transformed accordingly and applied in the simulator. The policy is trained using RL across diverse objects, optimizing a simple reward consisting of binary success and a collision penalty. flow-matching policy is trained on successful rollouts with rendered images to enable sim-to-real transfer.
We conduct large-scale experiments in both simulation and the real world to evaluate DemoGrasp. 3.4K objects from DexGraspNet (Wang et al.) 2023), DemoGrasp achieves success rates of 95% in state-based settings and 92% in vision-based settings, surpassing previous state-of-the-art method (Wang et al. 2025) by a large margin. DemoGrasp also exhibits strong transferability to a wide variety of robotic embodiments and generalization to unseen object categories. Trained on 175 objects, the policies achieve an average success rate of 84.6% on six unseen object datasets across various embodiments, including dexterous hands with different numbers of fingers, grippers, and arm-hand systems. In real-world experiments, DemoGrasp achieves success rate of 86.5% on 110 unseen objects, covering wide variety of geometries and visual appearances. For normal-sized objects, it achieves a superior success rate of 95.3%. Benefiting from the simple reward design, the policy is, to our knowledge, the first grasp previously unseen small, thin objects in tabletop settings without severe collisions, achieving a success rate of 71.1%. DemoGrasp also exhibits generalization to spatial, background, and lighting changes, and is extensible to various camera configurations (RGB and depth) and cluttered scenes, underscoring its practical applicability.
Our contributions are summarized as follows:
We propose DemoGrasp, simple yet powerful learning framework that addresses key challenges in learning universal dexterous grasping policies. With a novel formulation of demonstration editing and single-step RL, DemoGrasp enables robust policy learning, minimal reliance on reward shaping, and sim-to-real transferability.
DemoGrasp achieves tion and the real world, demonstrating strong capability in grasping unseen objects.
performance in large-scale evaluations in both simula-
We demonstrate the strong extensibility of DemoGrasp novel embodiments, camera configurations, and cluttered scenes, establishing a foundation for future research and applications in dexterous manipulation.
2 METHOD
2. PROBLEM FORMULATION
We consider grasping an arbitrary object from a large object set in tabletop settings. The task formulated partially observable Markov Decision Process (MDP) (Kaelbling al. 1998).
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Specifically, at each timestep t, the observation comprises the hand joint angles qland, the 6D pose of the end-effector (wrist) p. the 6D pose of the object Pt obj and a full object point cloud ob describing its geometry; the action comprises target hand joint angles gland and the target end-effector pose ft for the PD controller. The objective to learn a universal state-based policy
a π (dhaud Pt
haand
obj
qt Pi Pt Ct
that maximizes the expected cumulative reward Ei= y'r across objects, where vy denotes a
task reward encouraging successful grasping. T' is the time limit, and y is the discount factor. Since solving this multi-step MDP directly is challenging, we present our techniques in the following subsections, with the final formulation as a single-step MDP in Section 2.33
To enable sim-to-real transfer - where object poses and full object point clouds are not observable
on hardware we follow common approaches (Singh et 2024 Zhang et al. 2025b) that train vision-based policy
π vision ghand
Pr.
q.
hand ce vt)
to imitate the learned state-based policy, where it denotes visual input (e.g., RGB images, depth images, partial point clouds).
2.2 DEMONSTRATION EDITING
The high-dimensional actions, long task horizons, and multi-tasi nature training on all objects in the formulated MDP pose significant challenges for RI. exploration. We propose using a single demonstration to facilitate exploration.
The demonstration is trajectory of successful grasp for specific object in the simulator, which can be acquired either by teleoperation in the simulator or by executing hard-coded robot action sequence. We define the initial object frame a static coordinate frame obtained by translating the world frame to the object's geometric center at the first timestep of the demonstration. We then represent the robot actions the demonstration in this frame:
D {(ge shand
m.n-obij) T It=
Pt
where of ghand denotes the target hand joint angles and P. xee-ob denotes the target 6D end-effector pose expressed in the initial object frame. Intuitively, hgrda forms a hand-pon sodd-poseeee sequence from open to close, and {p, jee-obbj } is an end-effector trajectory that first approaches the object center (the origin of the initial object frame) and then lifts.
For arbitrary object placed any position in the simulator, replaying this demonstration an open-loop manner: (1) first, set the hand action to hhand and move the end effector to Po the robot's pose (in that frame) with the first step the demonstration; (2) then, for each timestep t = L....... TD, set the hand action to qi chand and transform p; tee-ob) back to the world frame as the end-effector's target pose in the world frame. This replay mechanism, which reuses the same hand grasp poses and wrist approach directions for all objects, already achieves non-trivial success rates when evaluated across all objects (see Table
attempt to grasp it by simply
+ec-obj under the new initial object frame via motion planning, aligning
Universal grasping necessitates more flexibility in motion patterns than replaying a single predefined trajectory. For example, for large objects with different graspable parts, thin, slippery objects
that require the fingers to reach under the object, the end-effector pose sequence {p. -ee--- should
be adjusted to change where to grasp. For objects with varied sizes and geometric features, the
hand action sequence {ạ. (hand), ) should be adjusted change how grasp. Hence, we introduce parameters for demonstration editing to adapt the demonstration to diverse objects. The parameters consist of end-effector transformation matrix Tee E SF(3) and delta joint angles for the hand
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Ag
Robot actions in the demonstration are then modified as:
Toe Pt
see-obj
I Tit.. otherwise,
eee-bbj
Pi
10 A=] PTia
e'ee-obj
shand +
Ag go hand
qi
hand
90
hhand + (ge
20 bahadd
Thaa 40
saand
1 I Tin
(2)
TTaa
''hand
otherwise.
Here, Tun denotes the first timestep at which the object's z-position increases (i.e., it begins to be lifted) in the demonstration. A= is constant vector the direction that lifts the object vertically after Tiitt End-effector target poses are modified by applying the transformation 7 in the initial object frame, changing the approach direction and offset toward the object center. Hand actions at each timestep are modified by interpolating between the initial open pose ha and the modified shand A the interpolation ratio is applied elementwise.
grasp pose 4iim
We denote the edited demonstration as = Edit( D. T G.. By varying T and Ag and replaying D' in simulation, the robot executes diverse, smooth action sequences that grasp the object at different positions, orientations, and hand poses, yielding an effective exploration scheme for universal grasping.
2.3 SINGLE-STER REINFORCEMENT LEARNING
MDP reformulation. Given the grasp exploration scheme via demonstration editing, we reformulate the task as single-step MDP: the policy outputs a single action specifying the editing parameters, after which the edited demonstration is replayed in the environment for maximum of I'' timesteps, and the environment returns a reward for the whole episode. Formally, the observation comprises the initial end-effector 6D pose po the initial object pose ohj and the full object point cloud cp The action consists of the end-effector transformation 7 and the delta hand grasp pose Aq used for demonstration editing. The transition replays the edited demonstration D' and then terminates.
The policy и (T Ags G ph :Po Co chi) aims maximize the expected single-step reward R(r)- In implementation, we represent end-effector rotations as quaternions in the observation space and as Euler angles in the action space, yielding a compact representation.
Reward design. With the compact, low-dimensional action space and the short horizon introduced by the one-step MDP, the exploration challenge is significantly mitigated, making complicated reward engineering unnecessary. We therefore use a simple reward that comprises grasp success and robot-table collisions, focusing the policy on collision-fre grasping:
[[success] [no collision during execution]
However, grasping flat objects on the table sometimes requires slight contact with the surface so that the fingers reach underneath the object. The strict collision-fre free objective may prevent success in these cases. To address this, we leverage IsaacGym's 's parallel simulation (Makoviychuk et al., 2021 to optimize across all objects simultancously and randomly disable robot-table collision detection in half of the environments, allowing hand-table penetration. In the reward, collisions are assessed via penetration of hand keypoints into the table. This design yields: (1) collision-free successful grasps achieve B(r] = 1; (2) successful grasps with robot-table contact receive E[r] 0.5; and (3)
failures receive E[r] 0. This encourages the policy to avoid unnecessary collisions while permitting
minimal contact when beneficial for hard-to-a sord-to-graa to-grasp objects.
2.4
VISION- BASED SIM-TO-REAL
After training the RL policy, we train a vision-based policy on its successful rollouts to enable sim-to-real transfer. We record robot proprioception (hand joint angles and end-effector poses), robot actions, and rendered RGB or depth images from successful rollouts to form a dataset. We then train Slow-Matching (Lipman et al.) 2022) policy with action chunking for imitation learning, modeling the mulli-modee action distribution with high quality. To close the visual sim-to-ree gap, we perform domain randomization of colors, textures, lighting conditions, camera extrinsics, and table positions
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Table I: Success rates on NexGraspNet with the Shadow Hand in simulation. Results are reported for both state-based and iision-based settings on 3,200 training objects (Train.), 141 unseen objects from seen categories (Test Seen Cat.), and 100 unseen objects from unseen categories (Test Unseen Cat.).
State-Bascd Setting (%)
Vision-Basse Setting (%)
Method
Test. Seen Cat.
Test. Unseen Cat.
Test. Seen Cat.
Test. Unseen Cat.
Train.
Train.
UniDexGrasy
79.4
74.3
70,8
73.7
68.6
65.1
UniDex Grasp++
87.9
84.3
83.1
85.4
79.6
76.7
UniGraspTranstinn Tranffommer
91.2
89.2
88.3
88.9
87.3
86.8
DemoGrasp
95.2
95.5
94.4
92.2
92.3
90.1
during data collection, and we finetune pre-trained ViT (Dosovitskiy al. (021) visual representation. Further implementation details are provided in Appendix E
encoder for the
3 EXPERIMENTS
Our experiments aim to evaluate: (1) the performance and scalability of our method through large- scale simulation with diverse object datasets and dexterous hand embodiments (Sections 3.2 and (33 (2) the sim-to-real performance of our method through real-world experiments with wide variety of objects (Section) 3.4): and (3) an analysis of the components of the proposed method (Section 3.5)
3.1 EXPERIMENTAL SETTINGS
Simulation. We IsaacGym (Makoviychuk et al. 2021 the training and evaluation platform for all simulation experiments. For evaluations on DexGraspNet (Wang et al. 2023) with the Shadow align with
Hand (Section (.2), we train 3,200 objects from the DexGraspNet training baseline settings. In all remaining sections, unless otherwise specified, we randomly sample 175 objects from the YCB dataset (Calli et al. 2013) and the DexGraspNet training set for training, and test on unseen objects from other datasets. For both training and evaluation, we randomize the object's initial position within 50 cm x 50 em region ensure spatial generalization of the policy. A trial is considered successful if the object's s center is raised at least 10 cm above its original position and the average distance between the object's center and hand keypoints less than 12 cm after the policy executes for fixed number of steps.
Real robot. We use a 6-DoF Inspire Hand (6 active and passive joints) mounted on a 7-DoF Franka Research 3 (FR3) robot arm for real-world experiments. Two RealSense D435i cameras are used to evaluate vision-based policies with either RGB or depth input, placed at two diagonal sides of the table. In simulation, the camera intrinsics match those of the real cameras, and the camera extrinsics are randomized around the calibrated real-camera extrinsics. Figure shows the hardware setup and the camera views.
3.2 RESULTS DEXGRASPNET
DexGraspNet Wang et al.) 2023) is widely used dataset for studying universal dexterous grasp- ing. We follow the settings of previous state-of-the-z arr methods UnniDexGrasp (Xu al., 2023). UniDexGrasp*+ (Wan et al. 2023). and UniGrraspTransfomme
(Wang et al.. 2025) training the 18-DoF Shadow Hand with 6- DoF floating wrist on the training set of 3,200 objects. T grasp success rates for DemoGrasp and prior methods. DemoGrasp surpasses the best bascline by 5% in state-based settings and 4% vision-based settings on both training and test sets, and exhibits a minimal generalization gap of 1% between training and unseen objects, demonstrating strong learning and generalization performance.
Notably, the baseline methods do not randomize object initial positions, whereas method is trained and tested with large reset region of 50 cm x 50 cm, posing a challenge for spatial generalization. Benefiting from the translation invariance of our demonstration--eeplay mechanism (i.e., replaying

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Table 2: Success rates across unseen datasets in simulation using the Allegro Hand mounted on a URS arm.
Dataset
DGA
EGAD
Omni6DPose
ModelNet40
VisualDexterity
Method
RobustDexGrasp
64.40
93.45
73.00
75.70
92.50
DemoGrasp
74.40
96.75
82.24
75.58
97.80
demonstration for the same object different initial locations leads to the same grasp outcome), spatial randomization does not hinder RL exploration in our method, yielding strong spatial generalization. In addition, while baselines rely on complex reward designs (e.g., hand-object distance, object-lift, and hand-lift lift terms) to facilitate RL, our method uses a simple binary reward, highlighting the simplicity and effectiveness of our approach.
3.3 SCALABILITY AND GENERALIZATION
Previous research tabletop dexterous grasping has typically evaluated a narrow set of datasets and a specific dexterous hand, leaving method scalability largely unassessed, [Zhon et al. (2025b) find that existing datasets, such as DexGraspNet, do not span the breadth of real-world graspable objects. Therefore, we conduct cross-dataset zero-shot tests to evaluate the generalization of universal grasping policies. We train the policy on 175 objects-75 randomly sampled from YCB (Calli et al..
2013) and 100 randomly sampled from the DexGraspNet training set (Wang et al. (202) and test on five -uitt-ddistriuttion datasets: DGA (Zhong et al. (0256), EGAD (Morrison et al. 2020) Omni6DPose (Zhang et al.] (2024b), ModelNet40 (Wu et al. 2015), and Visual Dexterity (Chen et al. 2023). For Omni6DPose and ModelNet40, which consist of larger objects, we randomly scale objects to sizes between cm and 15 cm for testing. A snapshot of objects from each dataset is provided in F
We evaluate DemoGrasp on various robotic hands without hyperparameter tuning, assessing its cross-embodiment universality. For the Allegro Hand mounted on UR5 arm, we compare against the RobustDexGrasp (Zhang al. 2025b) policy. Although trained on different object datasets, the test sets are unseen for both methods and thus form a fair comparison, since both aim at universal grasping over arbitrary objects. As shown in Table 2 DemoGrasp matches RobustDexGrras on ModelNet40 and surpasses it on the other four datasets, demonstrating the strong generalizability of the DemoGrasp policy.
We further extend the evaluation to various embodiments from Ding et al. (2024). in- cluding five-fingered hands (Inspire Hand, Shadow Hand, and Schunk SVH Hand), the four-fingered Allegro Hand, the three- fingered DClaw gripper, and a parallel grip- per. Figure 3 visualizes results for all hands the test datasets, and the quantitative results are reported in Table TO All multi- fingered hands achieve 90% success on the 175 training objects and generalize to unseen datasets with an average success rate of 84.6%, indicating that our method ex- tends easily to different hands rather than overfitting to particular hand. Notably, all six hands are mounted on robot arms; together with the follision-free training robotic embodiments across all test datasets. objective, this makes the trained policies more likely to succeed sim-to-real deployment compared with prior work using floating-wrist hands. Our results show that the Shadow Hand mounted on an arm (FR3+Shadow) underperforms the floating Shadow Hand (Shadow) by only 1.4% on average across the test sets, indicating that adding a robot arm does not harm performance. FR3+Gripper performs worse on EGAD and DGA, because the Panda gripper's 's limited stroke hinders grasping wide or large objects; in contrast, all multi-fingered hands achieve high success rates EGAD.
LGAD [49.4-97.9%]
DGA
[30.7-7933%
FR3 Imp te
LRS Allogro
FR3 DClae Shadow
FR3 Staddo
LBSS Schunk
FR3 Gripper
Dmni-6DDose [66.6-85.00
DexGraspPNet Unssen Cat.) (81.1-97.59
Mode/Net40
VisualDexterity
[61.2-80. 196
[83.5 (-3.5-99. 1%)
Figure 3: Success rates of DemoGrasp for various
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3.4 REAL- REAL-WORLD EXPERIMENTS
We evaluate the vision-based policies on real robot using 110 unseen real-world objects to assess generalization and sim-to-real performance. Images all objects are shown in Figure For each trial, we randomize the initial object orientations and positions within a 50 cm x 50 em region and count success when the object is lifted and held for two seconds. of DemoGrasp using two RGB views across different categories of real-world objects. It achieves an average success rate of 95.3% on normal-sized objects-includingg shapes, deformable objects, and irregular geometries- demonstrating strong generalization. For flat, thin objects (thickness < 1.5 cm), it achieves 68.3%; for small objects (diameter < 3.5 cm), achieves 76. 7%, demonstrating successful sim-to-real on these challenging tabletop grasping scenarios. Figure Z iillustrates trajectories in real-world tests, showing that DemoGrasp can achieve collision-free grasps for normal-sized objects, appropriately leverage finger-table contact to grasp small, thin objects, and exhibit regrasp behaviors to recover from failures in closed-loop manner.
the success rates
everyday items various
DemoGrasp also extensible to more advanced grasping tasks, such as cluttered grasping and catruction-folloiing by including random distractor objects and automatically generated language descriptions during vision-based data collection simulation. We evaluate the policies in both simulation and the real world. In simulation, we test on randomly sampled cluttered scenes; in the real world, we test on 10 cluttered scenes, each consisting of 5-8 randomly selected objects. Table 4 reports their success rates. The unconditional policy Any-DemoGrasp) counts success when conditioned policy
grasps any object from the clutter, whereas the counts success when it grasps the object specified by the instruction. Both policies achieve > 80% success in both simulation and the real world, highlighting the robust performance of DemoGrasp on challenging task settings. The senguagg-conditioon policy slightly outperforms the unconditional policy, suggesting that reducing action uncertainty for vision-based imitation learning can improve action quality. We further evaluate the inguaae--oonittioned policy under randomized backgrounds and lighting conditions, achieving an 82% % success rate, demonstrating robustness to scene appearance changes. Qualitative results are provided in Appendix D.2
Table 3: Success rates on 110 real-world objects. Each
object is tested for five trials with randomized initial poses.
Table 4: Results for grasping in clut- tered scenes. notes the unconditioned policy that grasps random object; "Instruct-
de-
Shape
Category
Num.
Success.
Bottles
12
95.0%
DemoGrasp" denotes the language- conditioned policy.
Boxes & Jars
22
93.6%
Regular
Balls & Fruit
12
98.3%
Soft Toys
10
96.0%
Model
Sim.
Real.
Irregular
18
90.0%
Any- DemoGrasp
83.66%
82%
Tools Others
10
60.0%
Flat & Thin
14
74.3%
Instruct-
85.33%
84%
DemoGrasp
Small
12
76.7%
3.5 ABLATION STUDY
The necessity of RL. A direct ablation for DemoGrasp is to replace RL with sampling-basee methods. Given the demonstration-ediiing scheme, we sample in the editing- parameter space for each object and position, execute the edited rollout in simulation, and train a behavior cloning (BC) policy on the successful rollouts. Table (JbleaScomppacc this sampling-he -based method with the RL-based method on the 175 training objects. We find that sampling+BC policies achieve significantly lower success rates than the RL policy. The main reason is that sampling can produce diverse successful rollouts for the same object and position, yielding multimodal and inconsistent dataset that hinders BC from converging to
Table 5: Sampling vs. RL. For the sampling- method, we uniformly sample editing parameters collect 35,000 successful trajectories, then train a behavior cloning (BC) policy.
Method Success (%)
Sampling
77.56
RL
96.24
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Table 6: Success rates of vision-based policies with different camera contigurations in simulation and the real world.
Simulator
Real Robot
Camera Config.
black bottle
blue box
little duck
tiny bottle
phone
YCB
DexGraspNet
case
Mono-Depth 80.2%
95.2%
5/5
4/5
1/5
0/5
0/5
Two-Depth
80.3%
96.4%
5/5
4/5
1/5
0/5
0/5
Mono-RGB 83.2%
94.8%
4/5
5/5
4/5
0/5
3/5
Two-RGB 87.0%
97.3%
5/5
5/5
4/5
5/5
5/5
Table Success rates on the test sets when trained on 175 objects from the training set (row 1) or trained directly the union of the test sets (row 2).
Test
Omni- 6DPose
Visual Dexterity
DGA
EGAD
ModelNet40
Train
175 Objects TYCB+DexGGrspNNe
65.62
97.88 99.16
85.04 88.71
80.13 81.10
99.13
Test Sets
71.49
99.20
optimal policy. In contrast, RL directly optimizes the expected return, resulting in more consistent, unimodal policy with higher success rates.
The action space in RL. We examine the effect of cach demonstration parameter RL performance, studying the contributions of wrist DoFs and hand DoFs. Table reports success rates for RL with different action-space components. Success rates consistently improve as the action space expands, showing that RL can effectively leverage the full dexterity of the wrist and hand to achieve higher performance. Editing end-effector translations and rotations is essential for grasping, contributing +6% and +13% to the training-set success rate, respectively; editing hand DoFs yields smaller +2% gain, indicating that using dexterous hands single-Dof grippers can already achieve high success rates in grasping. Figure 4 further shows that editing hand DoFs produces more robust grasps (c.g.. grasping a vase from the side and using the thumb, index, and ring fingers form force closure), whereas other ablations do not exhibit this behavior.
Table 8: Success rates of RL policies with different action spaces. Axyz, Arpy, and Åq denote the inclusion of wrist translation, wrist rotation, and delta hand actions in the RI action space, respectively. The first row corresponds to replaying the original demonstration without RL.
Axyz
Axyz + Ag
Axyz
Arpy
Åq
Training Set
Test Set
75.29
73.43
81.35
76.04
86.40
79.68
94.22
81.39
Axyz Arpy
Axyz + Arpy Ag
96.24
82.74
Figure 4: Learned grasps der different action spaces.
Camera configurations for vision-based policies. DemoGrasp enables sim-to-real deployment with different camera types by training vision-basee policies on correspondingly rendered data, Table 6 reports performance using either RGB or depth input, and either monocular or two-view configurations. We observe that two RGB views achieve the best performance in both simulation and the real world, outperforming the two-depth-camera configuration. Specifically, RGB policies achieve >70% success rates small, flat objects in the real world, whereas depth policies often fail. This because RGB policies can identify such objects via visual cues (e.g., color and texture), whereas depth policies may not distinguish the object from the tabletop due to sensor noise. Two-camera setups consistently outperform monocular ones: the former provides richer 3D information and reduces hand-object occlusions during grasping.

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Published as a conference paper at ICLR 2026
Table Success rates with different demonstrations. Demonstrations are collected via teleopera- tion to grasp objects of different sizes (small vs. large) and from different directions (top vs. side). While directly replaying the demonstrations yields widely varying success rates across all objects, all policies learned by DemoGrasp achieve comparably high success rates.
Success.
Demo Replay
RL Policy (Training Set)
RL Policy (Test Set)
Demo.
small obj. + top
75.29%
96.24%
82.74%
small obj. side
62.90%
95.18%
81.45%
big obj. + top
7.23%
95.02%
82.46%
big obj. side
3.88%
95.27%
83.22%
Are 175 training objects enough? Table shows that when trained directly on the five test sets, the policy achieves average performance gain of 2.4% relative to training on 175 objects and testing on these same test sets. This marginal gain suggests that universal grasping can be achieved with a small training set using DemoGrasp.
Demonstration quality. We study the effect of demonstration quality for DemoGrasp, using demonstrations that grasp different objects and approach from different directions. Results in Table show that DemoGrasp consistently achieves high performance given any successful demonstration.
9
4 CONCLUSION
DemoGrasp is an RL framework for universal dexterous grasping that leverages a single demonstration to mitigate the exploration challenge. By formulating single-step MDP with a compact action space for demonstration editing, DemoGrasp eliminates the need for complex reward shaping and can robustly achieve excellent success rates when trained on diverse robotic hand embodiments and object datasets. Extensive simulation experiments demonstrate that DemoGrasp achieves state-of-the-a success rates multiple dexterous hands and object datasets. Real-world experiments show that DemoGrasp achieves robust zero-shot sim-to-real transfer via simple vision-based imitation learning, successfully grasping diverse real-world objects, including small and flat items that have remained challenging in tabletop settings for prior work. Taken together, the contributions of DemoGrasp not only establish novel approach dexterous grasping but also provide an nasytto-implemnnt robust RL framework for robotics research and applications.
ACKNOWLEDGMENTS
This work was supported by NSFC in part under Grant 62450001 and 62476008. The authors would like thank the anonymous reviewers for their valuable comments and advice.
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A LIMITATIONS AND FUTURE WORK
Although our method handles universal dexterous grasping in both simulation and the real world and extends to cluttered and instruction-based grasping, some advanced tasks still require nontrivial design-such -uuhh as functional grasping and tightly cluttered scenes that require pre-grasp manipulation. In addition, while our policies exhibit some closed-loop regrasp capabilities through dision-based distillation, the policy learned in the RL stage is open-loop and cannot handle dynamic scenes or fine-grained manipulation. Future work could explore a trade-off between DemoGrasp and direct RL in the low-level action space to enable both efficiency and closed-loop behaviors. For example, by breaking demonstration trajectorics into short segments and having the RL policy operate at the segment level.
Using pure visual observations may limit the performance of our method, particularly when the dexterous hand occludes the object in camera images, when the real-world object requires grasping with appropriate strength, or when the task demands precise grasping for downstream manipulation (e.g., grasping an electric screwdriver with the index finger pressing the button). Incorporating tactile feedback presents a promising future direction to mitigate these issues. Future work could include tactile data in the observations and design reward functions to achieve more precise grasping. Given the sim-to-real challenge posed by tactile sensors, another promising direction is to collect real-robot data with tactile sensing using our vision- policy and then finetune with the collected data to incorporate tactile information.
B RELATED WORK
Universal Dexterous Grasping is fundamental task for robotic manipulation. Research approaches can be broadly classified into static grasp generation and dynamic grasping policy learning.
The goal of grasp generation is to synthesize hand and wrist poses of robust grasps, serving either
as targets for grasping motion planning (Wang et al., 2023 Zhang et al., (024a) or as auxiliary
information for policy learning (Xu al.. [202)). Recent studies have explored various strategies:
Weng et al. (2024) employ diffusion models to generate grasping poses: Zhong et al. (2025b) enhance
diffusion- based generation by incorporating physical constraints during sampling: Wei et al. (2024)
train a grasp generation model for multiple hand embodiments; Jian et al. (2025) leverage contact and affordance priors retrieved from existing grasp examples; and Chen et al. (2025a) propose a model that generates grasp poses conditioned on predefined grasp taxonomies. Beyond synthesizing physically plausible grasps, recent research in language-guide 2024) introduces the additional challenge of learning a joint distribution between natural language and dexterous grasp poses. However, deploying grasp- gesp-generattion models still requires manual motion- planning design and faces challenges in tabletop settings when collision-free grasp trajectory does not exist.
grasping (He et al., 2025 Li et al.,
Policy-learnig methods aim to learn closed-loop grasping policies over low-level robot actions, enabling direct deployment on real robots and avoiding explicit collision handling and path planning. Yet training universal grasping policies via RI. is challenging due to high-dimensional, long-horizon exploration, the need to optimize across diverse objects simultaneously. and the gap for closed-loop sim-to-real transfer. Prior work has explored curriculum learning (Xu et al. 2023 Chen al. 2025b).
policy distillation (Wan et al. 2023 Wang et al. 2025 Yuan et al. 2025a). residual learning (Huang
al. 2025 Zhao al. 2025). and -bject-geometry representation (Zhang et al. 2025a to mitigate
the RL exploration burden in simulation.
Recent advances include Trinsffrrransfomee WWang et al. 2025), which proposes a Transformer- based distillation method that can distill thousands of single-object policies without significant performance loss, achieving SOTA performance on DexGraspNet. However, still struggles with sim-to-real transfer. RobustDexGrasp (Zhang et al.) 2025b) trains performant policy for universal dexterous grasping using geometry representation and contact prediction, achieving sim-to-real transfer on a large number of objects. However, the method requires high-quality point cloud observations and is evaluated only on the Allegro Hand, limiting its ability to grasp small and thin objects. ClutterDexGrasp (Chen et al. 2025b) explores grasping in cluttered scenes using a similar geometry representation with a contrastive distance reward function, but it is evaluated on a specific hand and cannot grasp small objects in real-world environments. DexGrasp VLA (Zhong al.
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2025a) trains
grasping policy solely from real-world teleoperation data but is limited to large objects with a single grasp pose, making it costly to acquire training data and limiting object generalization. DextrAH-RGB (Singh et al. 2024) and DextrAH-G (Lum et al. (2024) explore sim-to-real techniques using RGB or depth images, but their real-world evaluations are limited to approximately regular-sized objects.
In our work, we address the challenge of designing Sigh-performanc,, efficient RL pipelines for grasping policy learning. We mitigate the exploration burden by introducing a single demonstration and reformulating the problem as a single-step MDP. As result, our method extends to any dexterous hand embodiment without hyperparameter tuning and achieves performant sim-to-real transfer across a wide variety of unseen objects, including small and thin ones, using only simple RGB observations.
Learning Robotic Manipulation from Demonstrations is an active research direction that leverages human priors from demonstration data to facilitate robot learning. Imitation learning methods directly
learn robot policies by imitating human teleoperated data (Zhao al. 2023 Fu et al., 22224] Chi et al.
2023) or human manipulation data (Luo al. 2025 Bi al. 2025 ) but require a large number of
high-quality trajectories. Some works (Mandlekar et al. 2023 Jiang et al. 2025 Xue et al. 2025
improve data efficiency for imitation learning by synthesizing multiple demonstration trajectories from single demonstration. Demonstrations signals (Chen et al. 2024, Zhou et al. 2024 Li al. 2025). auxiliary losses (Qin et al. 2022), and training data in the replay buffer (Luo al. 2024). In work, we introduce single successful grasp demonstration to facilitate RL for universal grasping. By appropriately transforming wrist and hand poses, this single demonstration augmented into diverse grasp trajectories for arbitrary objects, enabling a universal policy.
also be used to augment RL, serving reward
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C OBJECTS USED IN EXPERIMENTS
Figure 5 shows the 10 objects used in our real-world experiments. shows samples from each dataset used in our simulation experiments.
CHEEZ
REAREOUSE
Figure 5: Objects used in real-world experiments.
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Training Set
DexGraspNet (unseen cat.)
DGA
EGAD
T
Omni-
Visual Dexterity
ModelNet40
6DPose
Figure 6: Snapshots of the training and test datasets used in our experiments. 16 objects are randomly sampled from each dataset for visualization.
D ADDITIONAL RESULTS
D.1 QUANTITATIVE RESULTS
Table 10: Success rates of DemoGrasp for various robotic embodiments across all object datasets.
Training Set
DexGraspNet (Unseen Cat.)
Omni- 6DPose
Visual
Embodiment
DGA
EGAD
ModelNet40
Dexterity
FR3 Gripper
90.21
81.10
30.71
49.38
66.56
79.46
83.49
FR3 DClaw
96.96
94.17
79.28
96.37
74.63
61.16
97.94
URS Allegro
92.93
94.18
74.40
96.75
82.24
75.58
97.80
FR3 Inspire
96.24
97.5
65.62
97.88
85.04
80.13
99.13
URS + Schunk
95.46
87.29
47.97
90.70
78.19
73.43
88.59
Shadow
97,43
89.75
60.99
94.58
74.84
71.88
93.67
FR3 Shadow
95.33
87.40
59.90
93.64
76.51
67.48
93.40
D.2
QUALITATIVE RESULTS
Figure 7 shows real-world grasp trajectories for arbitrary objects. Figure 8 shows real-world tests under complex, randomized scene configurations using a language-condittoned policy.
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Time
Grasping trajectories from real-world tests. DemoGrasp learns distinct finger poses for large, small, and thin objects maximize expected success. Slight robot-table contact is leveraged to grasp tiny objects (second-to-last row). A regrasp behavior emerges when an attempt fails (last row).
Figure 7:
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"Grasp the green box"
"Grasp the green toy"
"Grasp the grey bottle"
"Grasp the purple toy"
"Grasp the red strawberry"
PH
"Grasp the yellow banana"
Time
Figure 8: Real-world tests the language-conditiones DemoGrasp policy in cluttered scenes with randomized object positions, language instructions, backgrounds, and lighting conditions.
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D.3
THE NECESSITY OF TWO-STAGE TRAINING
There several reasons motivating to adopt two-stage training pipeline (state-based RL for data collection followed by vision-based imitation learning) rather than directly training a vision-based RI policy.
0.8
man mm
mpo
0.4
PPO-state
PPO-vision (Wro hand)
PPO-vision
0.0
750
1250
Training Iteration
Figure 9: Comparison of RL training curves between the state-based policy (7000 parallel environ- ments) and the vision-based policy (175 parallel environments). "w/o hand" denotes reduced action space that includes only end-effector transformations, while others use the full action space.
0.4
175 envs
875 envs
0.2-
1750 envs
3500 envs
7000
0.0
500
75g
1000
1250
1500
1750
2000
Training Iteration
Figure 10: Comparison of RL training curves with different numbers of parallel simulation environ- ments, ranging from 175 to 7000.
First, large-scale parallel simulation is necessary for efficient RL training on robotic tasks. directly train vision- based RL policy, rendering RGB images consumes substantial GPU memory, resulting fewer than 200 parallel environments on single RTX 4090 GPU. In contrast, state-based training without rendering supports up 7000 parallel environments. The large number of parameters introduced by the vision encoder also increases training cost. Therefore, given the same computation budget, state-based RL allows significantly more parallel environments and leads to better training performance. To verify this, we implement a rision-based RL pipeline in which the policy takes two RGB images processed by pretrained ResNet- 18 encoder. Figure shows that state-based RL, which uses 40 times more parallel environments than vision- RL, outperforms it in both sample efficiency and final performance. Figure 10 compares training curves under different numbers of
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parallel environments and confirms the necessity of scage simulation (typically more than 1000 environments) for RL.
Second, our method reformulates RL demonstration editing, where actions modify wrist trans- formations and hand delta angles. However, real-world deployment requires closed-loop control in the robot action space (per-timestep end-effector and hand joint targets). Therefore, two-stage pipeline necessary: RL optimizes in the demonstration-edd -editing action space, and imitation learning distills the behavior into policy that directly outputs robot actions. Online distillation methods such as DAgger are not applicable in this setting because they require the teacher policy and the student policy share the same action space. For this reason, we collect successful rollouts from the teacher policy and train the student policy using imitation learning on the offline dataset. This two-stage
RL+BC approach is also used in prior work (Wang et al. 2025 Yin al. 2025
Liu al. 2025).
D.4 TRAINING CURVES
0.8
0.6
Reward
Success Rate
Collision Rate
250
500
750
1000
1250
1500
1750
2000
Taining
Figure 11: RL training curves for the Inspire hand, showing total reward, success rate, and collision rate.
E IMPLEMENTATION DETAILS
E. DATASETS AND SIMULATION
In Sec. 3.2. we compare our method against prior approaches for universal dexterous grasping, all trained and evaluated on DexGraspNet (Wang et al. 2023). Following their protocol, we adopt the same split consisting of training set and two test sets. The training set contains 3,200 objects spanning diverse shapes and categories. The first test set includes 141 objects from categories seen during training but with novel shapes present in the training set. The second test contains 100 objects whose shapes and categories are both unseen during training, thereby assessing generalization entirely novel object classes.
For the experiments in Sec. 3.3 and the real-world experiments, we adopt a smaller training set. Specifically, we randomly sample 100 objects from the DexGraspNet training set and add 75 objects from YCB to increase shape and category diversity, yielding 175 training objects in total. We evaluate the learned policies on multiple datasets, including DexGraspNet (the 100 unseen- category objects) (Wang et al. 2023), DGA (Zhong et al. 2025b). EGAD (Morrison et al., 2020). Omni6DPose (Zhang al. 2024b), ModelNet40 (Wu et al. 2015). and Visual Dexterity (Chen et al. 2023). The results demonstrate robustness across diverse embodiments and datasets.
We use IsaacGym (Makoviychuk et al. (02T) as our simulation platform. For state-based policies, we sample 512 points from mesh vertices and surfaces for each object to form its full point-cloud representation. We increase the resolution of VHACD decomposition to 300K so that the objects' collision meshes in the simulator closely match the original meshes. We use hierarchical position controller for arm and hand control, where a high-level controller receives target joint positions and interpolates from the previous target the new target, sending smooth trajectory low-level PI)
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controller. We set the PD controller stiffness to large values and limit the step between consecutive target joint positions to produce smooth motion and accurately track the policy's 's actions. For robot hands mounted on arms, we use inverse kinematics to convert the policy's 's end-effector action outputs into target joint positions for the controller. Detailed simulation and control parameters are provided in TT]
Table 11: Simulation parameters.
Name
Value
Low-level control frequency
60 Hz
Policy control frequency
3 Hz
Simulation substeps
2
Object friction coefficient
1.0
Maximum arm angular velocity Maximum hand angular velocity Maximum hand joint effort Arm joint stiffness K amm
1.57 rad/s 6.28 rad/s
1.0
16000
Arm joint damping Kam Hand joint stiffness K Khand
600
600
Hand joint damping K haand
d
20
At the beginning of each replay of the edited demonstration, we require motion planner that can smoothly and accurately move the robot from its initial pose to the starting pose in the edited demonstration within the initial object frame. To achieve this, we use an interpolation- planner for the robot end-effector, where positions are linearly interpolated with a maximum step size of 0.04 m, and rotations are interpolated using SLERP with a maximum step size of 0.1 rad. Each interpolated pose serves as a target for the PD controller, which is executed for 20 simulation steps to reach the target. For the dexterous hand, we directly set the action to the initial joint positions from the demonstration. We verify that the robot can follow the action sequence with minimal tracking error using this motion planner both simulation and the real world.
motion
E.2 TRAINING RL POLICIES
The demonstratioo-eediiig policy is trained jointly across all objects in the training set using Proximal Policy Optimization (PPO) (Schulman et al. 2017). Full object point clouds are encoded with PointNet (Qi et al., 2017). and the resulting 128 22-diimesiional features are concatenated with the end-effector pose and the initial object pose in the world frame. This combined vector is then fed into the actor and critic networks, implemented as MLPs with hidden layers of sizes [1024, 1024, 512, 512] and ELU activations (Clevert et al.) 2015). For the final action output layer, we apply tanh to bound outputs to [-1, 1]. then rescale to the allowed editing ranges: end-effector translation Axyz [-0.05, 0.05] m; end-effector Euler angles Arpy E [-1.57, 1.57] rad; and delta hand joint angles åq E [-1,1] rad. The hyperparameters used for training are summarized in Table 12
Training converges within 24 hours single NVIDIA RTX 4090 GPU.
E.3 TRAINING VISION-BASED POLICIES
We collect 35,000 trajectories with the trained RL policy across all training objects and retain the successful trajectories to train the vision-based policy.
To bridge the sim-to-real gap in RGB images. we align camera and table configurations in simulation with those in the real world and apply broad domain randomization. We first estimate camera extrinsics via hand-eye calibration and set both camera intrinsics and extrinsics in simulation to match the real hardware. We then randomize camera extrinsics by adding uniform noise in the range [-0.02, 0.02J. We fetch 300 background images from RoboTTii (Mu et al. 2024) to sample table textures. For objects, we randomly sample colors and apply textures from 100 material images in FMD (Sharan et al.) 2014). We use three point lights and randomize their intensity and ambient parameters within (0.1, 0.8] to create varied lighting conditions. We also translate the table randomly within [-0.55, 0.05] increase background variation. For ablation studies using depth images,
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Table 12: Hyperparameters for RL.
Name
Value
Parallel environments
7,000
Initial actor Gaussian std.
0.8
Learning rate
3e-4
PPO clip range (e) Gradient-norm clip
0.2
1.0
Observation clip range Episode length for RL Execution steps for demo replay per episode
5.0
1
40
Rollout steps per iteration Update epochs per iteration Minibatches per epoch
1
5
4
follow HERMES Yuan al. 2025b) to augment the rendered depth observations. We randomize the depth range, add Gaussian blur and Gaussian noise, randomly set 1 % of depth pixels to zero, and blend each frame with random depth image from the NYU-Depth-v2 dataset (Nathan Silberman & Fergus 2012) using a = 0.005.
We adopt the GROOT-N1.5 (Bjorck al., 2025) architecture, consisting of pretrained Vision Transformer (ViT) encoder and flow-matching action head. We do not use the pretrained weights from GROOT-NI.5. instead, we train the action head from scratch and fine-tune the pretrained ViT. The model is trained for 100k iterations on four NVIDIA A800 GPUs, taking 16 hours.
To train language-conditionel policy for grasping in cluttered scenes, we sample distractor objects and construct language instructions during data collection. For each environment, we load 10 objects sampled from the training dataset. For each episode, we uniformly sample E [0, 10) objects among them as distractors and initialize them at random poses on the table. We use the instruction template "Grasp the (COLOR) {OBJECT NAMEJ." where {COLOR) the color name sampled for the task object and (OBJECT_NAMIS is its instance name from the dataset. We use the pretrained vision- language model open-sourced by GROOT-N1.5 to process language instructions during imitation learning, keeping its weights frozen. In real-works tests, use the true color and name of the object (which may be unseen during training) to prompt the model.
E.4
REAL- REAL-WORLLD WORLD EXPERIMENTS
We conduct real-world experiments using a Franka Research robot arm with Inspire Hand. The world frame is defined at the arm's 's base frame. Two RealSense D435i cameras are placed at fixed viewpoints to capture RGB or depth images for the vision-based policies. Figure 12 illustrates the hardware setup and camera views.
F AN ILLUSTRATION OF DEMONSTRATION EDITING
Figure 13 illustrates how the learned policy edits the demonstration of grasping a little duck to grasp a plate.
G USE OF LARGE LANGUAGE MODELS
In this work, the large language model (LLM) is used exclusively for text polishing. Its role is limited to refining the linguistic quality of the textual content, with no involvement the method or experimental results.
H OPUNCE COMMITMENT
We committed to open-sourcing the code upon paper publication.
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camera 1
Dopth
D
RGB
REB
camo
Figure 12: Example images from different camera sensors used in our experiments (left) and our hardware setup (right).
A demonstration of grasping duck.
The RL policy's action.
Aryz (0.0435, -000165,-0.00..... Arpy = (-0.164, 1.3300,-1455)) rad Aq (-0.414, 0.973, .... 0.996) rad
End-effector target poses demo
Hand target poses demo
Editing: apply transformation
Áxyz and Arpy
Editing: grasping pose +iq
Edited end-effector target poses
Edited hand target poses
Replay the edited demonstration grasp plate.
Figure 13: An illustration of demonstration editing using the learned RL policy.
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Reviews:
[{"review_id": "CEdt41UXPr", "reviewer": "Reviewer_uPY2", "summary": "This paper presents DemoGrasp, a framework that first augments demonstrations with pose and object randomization and a reinforcement learning (RL) residual policy, and then distills the results into an imitation learning policy. Extensive experiments are conducted in both simulation and the real world. The ablation studies on RL-sampling comparison, camera configuration, and RL action spaces are well designed.", "questions": "- It would be helpful to discuss more recent related works in the paper.  \n- The quality of figures and videos can be improved, preferably with higher resolution.\n- It would be good to include an open-source commitment in the paper to increase its reproducibility.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The experiments are extensive, and the real-world demonstrations looks robust.  \nThe analysis of RL-sampling comparison, camera configuration, and action spaces is insightful.  \nRL residual learning and RGB sim-to-real transfer are challenging, and the paper addresses them successfully.  \nThe presentation of the paper is clear and well structured.", "weaknesses": "- One weakness is that more details about the experimental settings should be provided, like RGB randomization details in the paper.\n- It is suggested to replace some edited videos on the website with uncut, continuous grasping videos.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rc4FL3o7Gs", "reviewer": "Reviewer_QqKj", "summary": "The paper shows framework for dexterous grasping policies using RL with single demonstration augmented. The policy could grasp a huge amount of objects and its appreciate the author conduct steps for sim-to-real, instead of simply reply the trajectory in simulation. The trajectory dataset and the sim-to-real for these trajectories could be a good contribution.", "questions": "See weakness above", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "Appreciate the sparse reward and simple collision penalty. Easy to read and follow the idea. The presentation is good. The experiments are comprehensive.", "weaknesses": "Minor weakness: \n\n1. the abstract contain many contents but not organize well. It get reader confuse that the author using RL in simulation but directly switch to imitation learning in the real-world without any context.\n\n2. line 090, please specify what is the \"prior methods\" mentioned, eg cite the corresponding paper.\n\n3. line 105-106, please specify what is \"previous state-of-the-art-methods\" here.\n\n4. Front size in figure 1 is too small to recognize.\n\n\nMajor weakness:\n\n1. when perform sim-to-real transfer, the author select the successfully RL rollout, then apply an additional training stage and apply domain randomization in imitation stage. why not directly train a vision based policy and directly transfer to the real. \n\n2. For two stage training, why select stage based RL and do a selection and then do BC? Have you ever research train a state based RL first and then perform a teacher-student pipeline to distill a vision policy, which can also be able to transfer to real.\n\n3. For the sim to real, the author mention the domain randomization for the RGB and depth. Have you apply any method on robot control side. eg. simulation hand is usually more compliant than real hand, especially for the inspire hand, which may like to broken if the collision is too large. Also for the arm, have you notice any control gap between sim and real.\n\n4. Would be more interested the pipeline and application to extend to beyond grasping task. \n\n5. How much randomization for the object initial pose? For a simple demo augumentation, does the larger randomization area decrease the max success rate of RL? \n\n6. While appreciate conduct the BC for sim-to-real, more experiments to justify the BC training would make experiments more comprehensive. Eg. better generalization, or better design choice, or help on implementation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "G9b6poJxuQ", "reviewer": "Reviewer_mozF", "summary": "This paper presents a method to learn dextrous grasping from just a single demonstration. The key differentiator with existing approaches is to formulate the problem as a 1 step markov decision process where the policy predicts a residual trajectory acting as a demonstration editor. The entire trajectory is treated as a single action executed using a lower level motion planner. This makes exploration and reward design much more simpler than imitating the entire trajectory. Once a state-based policy is trained in simulation, a vision-action dataset is collected to then learn a vision-based policy using behavior cloning.\nThe results show generalization to novel objects as well as zero shot transfer to real world robots.", "questions": "1) The problem formulation is not too clear - In section 3.1, the authors mention that the goal of the policy is to maximize the discounted sum of returns (like standard RL), and the equation suggests that the horizon “T” is the length of the demonstration trajectory. But this is at odds with the single step MDP that the authors claim in the beginning. Even in table 12, the episode length is set to 1. How exactly is the problem formulated and what is the discount factor?\n\n2) What is the motion planner used? How robust is the policy to the motion planner?\n\n3) The vision based policy is closed loop per grasp attempt, correct? What is the frequency of that loop? \n\n4) It would also be great to know how tactile feedback can be incorporated into this framework in future work section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper is well structured and easy to follow. It provides sufficient background, comparison to existing literature and clearly establishes the motivation for the paper. \n\nThe paper provides sufficient details about training, provides comprehensive quantitative comparison against relevant baselines across different object datasets and robot embodiments. The results and the supplementary videos show effective grasp success rates. \nThe paper also presents in depth ablation studies that sheds light on the necessity for RL instead of sampling based methods, the action space used in RL, camera configurations as well as demonstration quality. The proposed method performs better than baselines in all scenarios. \n\nAn additional strength of the paper is real-world results which is the ultimate test for any robot learning method.", "weaknesses": "While the results are promising, I think the biggest weakness is the lack of generalizability of the proposed method to more dynamic environments where a reactive policy might be required to avoid collisions or adjust a grasp mid motion.\n\nAdding details about how the language conditioned policy is learned can be extremely beneficial for a reader. Similarly, more details on the lower level motion planner that executes the trajectory would be really helpful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gm3e17Bcf4", "reviewer": "Reviewer_kYw5", "summary": "This paper presents a method for training dexterous grasping policies from a single demonstration in simulation. The key idea is to frame the problem as demonstration editing, where a reinforcement learning (RL) policy learns to generate residual corrections that enable grasping of novel objects. This formulation helps address the exploration challenge and leads to more efficient learning. Extensive real world evaluations are carried out to validate the method.", "questions": "See Weakness 1. I would like to see more discussion on this.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper is well-written and easy to follow.\n\nThe proposed method is conceptually sound and addresses an important problem in dexterous manipulation.", "weaknesses": "1. It is unclear what range of novel objects this approach can effectively handle given only a single demonstration on one object. For example, if the demonstration involves grasping a bottle, it may be difficult to transfer that experience to grasping a flat plate. It would be valuable to include experiments illustrating which types of object transfers are successful, and to discuss how such transferability might be predicted a priori.\n\n2. The computational efficiency of the proposed method appears to remain high compared to sampling-based or template-based approaches. Including quantitative comparisons of computational cost and a discussion of when this approach would be preferable to these alternatives would strengthen the paper.\n\n3. The high-level idea is not that surprising, given recent papers like DexMachina.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)