DexGraspFlow: Topology-Aware SE(3) Flow Matching for Cross-Embodiment Dexterous Grasp Generation

Bin Zhou; Boyu Ma; Xinyu Zhou; Jiahua Dong; Chuhao Zhou; Yizhou Liu; Hao Wan; Jianfei Yang

cs.ROOct 18, 2026

DexGraspFlow: Topology-Aware SE(3) Flow Matching for Cross-Embodiment Dexterous Grasp Generation

Authors: Bin Zhou, Boyu Ma, Xinyu Zhou, Jiahua Dong, Chuhao Zhou, Yizhou Liu, Hao Wan, Jianfei Yang

Organizations: MARS Lab, Nanyang Technological University, Singapore · ROKAE Robotics

Abstract

Differences in hand geometry, joint parameterization, and kinematic topology pose challenges for cross-embodiment dexterous grasp generation. However, spatial relations among links alone are insufficient to fully capture the hand's kinematic topology. We propose DexGraspFlow, a conditional link-level $\mathrm{SE}(3)$ flow-matching framework integrating geometric relations and kinematic topology. The framework encodes local object geometry, link geometry, and current poses. A dual-stream DiT models link--link coordination and link--object interaction, while attention biases derived from kinematic-tree distances provide explicit structural information for modeling evolving geometric interactions. The model jointly learns translational and rotational velocity fields, integrates them to generate link poses, and recovers joint configurations through inverse kinematics. Additionally, inference-time palm-orientation guidance enables controllable grasp generation without retraining. Across three robotic hands and 30 object--hand pairs, DexGraspFlow achieves a 95.33% average simulation success rate, with an average generation latency, including inverse kinematics, approximately 49% lower than that of a state-of-the-art baseline. Further experiments demonstrate zero-shot adaptability to seven finger-removal variants and palm-orientation control. A model trained on demonstrations for the deployed hand achieves a 91% success rate over 100 real-robot grasping trials, supporting the framework's applicability to physical grasp execution.

Figures & tables

Figure 1: Overview of DexGraspFlow. (a) Geometry and state encoding: Object points and per-link hand point clouds are encoded into object-patch tokens and static link tokens, while the kinematic tree defines the topological distance matrix $\mathbf{D}_{\mathrm{topo}}$ . Static link tokens are concatenated with dynamic link-pose features to form link-state tokens. (b) Geometry- and topology-aware dual-stream DiT: Link–object cross-attention and link–link self-attention process state-dependent geometric relation tokens, with kinematic-tree distance biases injected into the link–link stream, to jointly predict translational and rotational velocity fields $v_{\theta}=(v_{\theta}^{x},v_{\theta}^{\omega})$ with per-link outputs $(\hat{\mathbf{u}}_{i}^{x},\hat{\mathbf{u}}_{i}^{\omega})$ . (c) Flow to executable grasp: Continuous velocity fields are numerically integrated from prior distributions to generate target link poses, which are mapped into executable joint configurations via batched inverse kinematics.

Figure 2: Geometry-aware dual-stream attention in the DiT. The left branch performs link–link self-attention with physical-relation and topology biases. The right branch performs link–object cross-attention from link queries to object patch tokens with a gated physical-relation bias; topology bias is not used in this branch. In both branches, relation-aware pairwise features are projected to value messages before weighted sum.

Figure 3: Illustration of topology-distance construction from the URDF-derived physical-link graph. Each valid hand link is treated as a vertex in the undirected graph, and the distance between two links is the shortest-path length. The right panel shows an illustrative $3\times 3$ submatrix of $\mathbf{D}_{\mathrm{topo}}$ ; the colors indicate schematic link categories.

Figure 4: Inference-time palm-orientation guidance. The parallelogram depicts

\tilde{\mathbf{u}}_{t,p}^{\omega}=\hat{\mathbf{u}}_{t,p}^{\omega}+\Delta\mathbf{u}_{t,p}^{\omega}

, where

\Delta\mathbf{u}_{t,p}^{\omega}=s(t)\lambda_{c}\mathbf{R}_{t,p}^{\top}\bm{\omega}_{t}^{c}

. Solid and dashed curves illustrate sampling with and without the correction from the same initial state, with endpoints shown before inverse kinematics. “Small POE” indicates a small orientation error relative to the target

\mathbf{R}_{p}^{c}

. Only the palm trajectory is shown, although all valid links receive the correction.

Method	Success Rate (%)				Efficiency (s) $\downarrow$
Method	Barrett	Allegro	ShadowHand	Avg.	Barrett	Allegro	ShadowHand	Avg.
DFC ( Liu et al., 2021 )	86.30	76.21	58.80	73.77	$>1800$	$>1800$	$>1800$	$>1800$
GenDexGrasp ( Li et al., 2023 )	57.40	43.10	42.30	47.60	12.933	18.347	16.540	15.940
DRO-Grasp ( Wei et al., 2025 )	86.60	91.20	81.60	86.47	0.523	0.485	0.856	0.621
TRO-Grasp ( Fei et al., 2026 )	92.40	93.30	98.10	94.60	0.254	0.247	0.269	0.257
DexGraspFlow (Ours)	92.10	98.40	95.50	95.33	0.126	0.134	0.136	0.132

Table 1: Comparison of unconditioned grasp success rates and single-sample end-to-end generation latency across three hand embodiments.

Panel of Figure 5 — Figure 5: Representative flow-matching trajectories for three hand–object pairs. The rows correspond to Allegro, Barrett, and ShadowHand; the associated objects are a YCB tomato soup can, a ContactDB door knob, and a ContactDB apple, respectively. Blue images show selected states along the trajectory from $t=0$ to $t=1$ , while the final column shows the executable grasp obtained after Pyroki inverse kinematics.

Figure 6: Intact and topology-modified hand embodiments used for zero-shot evaluation. The arrows indicate the transition from each intact hand to the corresponding missing-finger variants. Here, I, M, R, and L denote the index, middle, ring, and little fingers, respectively.

Allegro			ShadowHand
w/o I	w/o M	w/o R	w/o I	w/o M	w/o R	w/o L

Figure 7: Representative zero-shot grasp examples on topology-modified hands. The columns correspond to Allegro with the index, middle, or ring finger removed and ShadowHand with the index, middle, ring, or little finger removed. From top to bottom, the rows show a baseball, pear, potted meat can, and water bottle.

Hand variant	DRO-Grasp	TRO-Grasp	Ours
Allegro w/o I	53.60	11.80	97.00
Allegro w/o M	61.40	45.80	75.10
Allegro w/o R	49.90	65.40	94.50
ShadowHand w/o I	47.80	2.50	89.00
ShadowHand w/o M	55.40	4.00	90.60
ShadowHand w/o R	57.90	7.20	82.80

Table 2: Zero-shot unconditioned grasp success rates (%) on topology-modified hands. I, M, R, and L denote the index, middle, ring, and little fingers, respectively.

Hand-wise results
Setting	Success Rate (%)			POE (deg.)
Setting	TRO	Ours	$\Delta$ SR $\uparrow$	TRO	Ours	$\Delta\mathrm{POE}$
Allegro	80.42	95.17	+14.75	38.25	15.18	+23.07
Barrett	77.17	83.50	+6.33	41.40	9.12	+32.28
ShadowHand	63.67	81.42	+17.75	35.51	17.46	+18.05
Average	73.75	86.69	+12.94	38.38	13.92	+24.46

Table 3: Six-direction conditioned grasp generation results. TRO and Ours refer to TRO-Grasp and DexGraspFlow, respectively. SR and POE denote success rate and final palm-orientation error, respectively.

\Delta

SR denotes the success-rate difference in percentage points, while

\Delta\mathrm{POE}=\mathrm{POE}_{\mathrm{TRO}}-\mathrm{POE}_{\mathrm{Ours}}

denotes the absolute POE reduction in degrees; positive values favor DexGraspFlow.

Panel of Figure 8 — Figure 8: Palm-orientation-conditioned grasp examples for a representative held-out rubber duck. The rows correspond to Allegro, Barrett, and ShadowHand, while the columns show the front, back, left, right, up, and down target directions. Blue and red arrows denote the target and post-IK actual palm approach directions, respectively.

Bias Configuration	Barrett	Allegro	ShadowHand	Avg.
No Attention Bias	86.10	98.30	75.50	86.63
Geometry Bias Only	87.50	99.50	82.10	89.70
Topology Bias Only	88.70	99.50	90.00	92.73
Geometry + Topology	92.10	98.40	95.50	95.33

Table 4: Ablation of structural attention biases under unconditioned generation. All values denote success rate (%).

Steps	Success Rate (%)				Time (s) $\downarrow$
Steps	Barrett	Allegro	ShadowHand	Avg.	Time (s) $\downarrow$
1	50.90	60.30	76.20	62.47	0.0130
5	93.40	98.10	92.60	94.70	0.0205
10	92.10	98.40	95.50	95.33	0.0257
15	91.70	97.80	93.50	94.33	0.0321
20	92.90	97.90	94.80	95.20	0.0361

Table 5: Effect of ODE integration steps on unconditioned grasp success rate and batch-amortized latency.

Rotational Guidance $\lambda_{c}$	Avg. SR (%)	Avg. POE (deg.)
$5$	89.08	22.135
$10$	87.33	17.552
$20$	86.69	13.921
$30$	83.50	12.225

Table 6: Sensitivity to rotational guidance multiplier

\lambda_{c}

under six-direction conditioning. Bold indicates default setting.

Panel of Figure 9 — Figure 9: Real-robot experimental setup. (a) Robot workspace view. (b) Close-up of the hand–object interaction.

Panel of Figure 10 — Figure 10: Real-robot grasping examples generated by DexGraspFlow for ten objects unseen during training using the Linker Hand L20Lite.

Object	Success/Trials	Object	Success/Trials
Can	7/10	Coil	9/10
Toy	10/10	Foam Box Lid	10/10
McCafe	10/10	Orange	9/10
Packaging Box	10/10	Pill Bottle	10/10
Pomegranate	8/10	Red Tape	8/10
Overall			91/100

Table 7: Real-robot grasping results on 10 unseen objects.

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Open paper AskPDF arXiv

Generating high-quality dexterous grasps remains challenging for learning-based methods, which often depend on carefully tuned contact losses or costly contact-based test-time refinement. We present KPGrasp, a flow-matching framework that learns dexterous grasp priors from large-scale data rather than relying on contact losses or contact-based test-time refinement. KPGrasp couples an all-Euclidean 3D hand-keypoint parameterization with a simple yet scalable Transformer flow model. The parameterization avoids the drawbacks of the conventional mixed SE(3) pose and joint-angle output space, expresses grasps in the same frame as the object point cloud, and thus enables native spatial reasoning; the Transformer flow model is trained with only the standard flow-matching loss and scales effectively with data, model capacity, and batch size. Experiments demonstrate state-of-the-art performance on two simulation benchmarks. On the Dexonomy benchmark, it reaches a 76.3% grasp success rate, improving over the strongest directly comparable baseline by 47.4% while reducing penetration depth to 2.4 mm. The same model also achieves the best average performance on the DexGrasp Anything benchmark without fine-tuning. For batched inference, KPGrasp requires only 0.032 s per grasp. Finally, real-world experiments on 20 diverse objects demonstrate that the pipeline can be deployed in a real-world setup. Project Page: https://yuansen-huang.github.io/KPGrasp-page/

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