DexGraspFlow: Topology-Aware SE(3) Flow Matching for Cross-Embodiment Dexterous Grasp Generation
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
DexGraspFlow introduces a topology-aware SE(3) flow matching framework that significantly advances cross-embodiment dexterous grasp generation by explicitly encoding kinematic structure, achieving state-of-the-art success rates and efficiency with strong real-world validation.
Methodology
The paper proposes DexGraspFlow, a framework for cross-embodiment dexterous grasp generation using SE(3) flow matching. The core methodological contribution is the integration of kinematic topology (via URDF-derived shortest-path distances) into the attention mechanism of a dual-stream DiT architecture. This addresses a specific gap in prior work (like TRO-Grasp) where spatial proximity was used as a proxy for kinematic relevance, which fails across different hand morphologies. The use of flow matching over diffusion models is a timely choice, offering faster inference (10 steps vs. 50+ for diffusion) and stable training. The formulation of separate translational (Euclidean) and rotational (SO(3) geodesic) velocity fields is mathematically sound and preserves the manifold structure of rotations. The inference-time palm-orientation guidance via tangent-space velocity corrections is a clever, training-free extension that enhances controllability.
Results & evaluation
The experimental evaluation is rigorous and comprehensive. The authors benchmark against strong, recent baselines (TRO-Grasp, DRO-Grasp, GenDexGrasp) across three distinct robotic hands (Barrett, Allegro, ShadowHand) and 30 object-hand pairs. The success rate of 95.33% is state-of-the-art, outperforming the previous best (TRO-Grasp) by a significant margin. The latency improvement (~49% lower) is substantial and practically important. The zero-shot generalization to finger-removal variants is a strong test of the topology-aware representation, showing the model understands the structural changes. The real-robot experiments (91% success rate) provide crucial validation beyond simulation, although the sample size (100 trials) is modest. The ablation studies clearly demonstrate the necessity of both geometric and topology biases.
Reproducibility
The paper provides detailed implementation specifics, including dataset splits, hyperparameters (learning rate, batch size, network dimensions), and evaluation protocols (Isaac Gym settings, force magnitudes). The use of standard datasets (CMapDataset, ContactDB, YCB) and open-source tools (Pyroki, Isaac Gym) enhances reproducibility. However, the code and model weights are not explicitly linked in the provided text (though typically available for such papers), and the specific "filtered CMapDataset split" might require clarification if not publicly released.
Limitations
The primary limitation is the reliance on a fixed set of training hands; generalization to completely novel hand morphologies (e.g., a 3-fingered hand vs. 5-fingered) is not tested, only finger removals. The method does not explicitly enforce joint limits or collision avoidance during the flow generation, relying on post-hoc IK which can fail or produce unnatural poses if the generated link poses are infeasible. The real-robot evaluation is limited to a single hand type (Linker Hand) and a small number of objects.
Broader impact
This work has significant implications for the field of dexterous manipulation. By providing a fast, accurate, and topology-aware method for cross-embodiment grasp generation, it facilitates the transfer of manipulation skills across different robotic platforms. The flow matching approach is likely to become a standard for generative robotics tasks due to its efficiency. The training-free guidance mechanism offers a practical way to add controllability to generative models without retraining, a valuable technique for other robotic applications. DexGraspFlow introduces a topology-aware SE(3) flow matching framework that significantly advances cross-embodiment dexterous grasp generation by explicitly encoding kinematic structure, achieving state-of-the-art success rates and efficiency with strong real-world validation.
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