Jun 2026· arXiv.org· Vol abs/2606.29924· 0 citations· 68 references
Computer Science
TL;DR
This work establishes a robust and versatile pipeline for the synthesis of controllable 3D hand-object interactions, built on a novel grasp energy term and shows that DCGrasp produces high-quality, physically plausible grasps with flexible user control.
Abstract
Generating 3D hand-object interactions is essential for applications in robotics, XR, and synthetic data generation, where flexible controllability and strong generalization to diverse object geometries are required. However, existing methods rarely satisfy these requirements, limiting their practical applicability. We present DCGrasp, a distance-aware controllable grasp generation system built on a novel grasp energy term. This term computes Distance Profile, a signed distance from each hand vertex to the nearest object point, coupled with distance-aware weighting, effectively capturing the semantically similar hand-object interaction in near-contact regions while remaining invariant to object and hand identity. Given various controllable signals, DCGrasp first generates a Distance Profile based on a Diffusion Transformer, together with a corresponding candidate hand pose. We then refine the candidate pose through optimization, enforcing consistency between the optimized hand pose and the generated Distance Profile in near-contact regions. Our experiments show that DCGrasp produces high-quality, physically plausible grasps with flexible user control, generalizing to diverse object and hand shapes and scales. Our work establishes a robust and versatile pipeline for the synthesis of controllable 3D hand-object interactions.
GraspGraphNet is introduced, a topology-aware grasp generation framework that represents each hand as a URDF-derived kinematic graph and directly generates executable palm poses and joint configurations and suggests that graph-structured hand representations can effectively support dexterous grasp generation across robot hands with different kinematic structures.
Y. Lee, Taeyeop Lee, Hyosup Shin et al.· 0 citations
Recent data-driven methods for synthesizing 6-DoF grasp poses use generative models to learn complex grasp pose distributions and generate diverse candidate poses. In particular, SE(3)-equivariant flow-based models generate grasp poses that transform consistently with object rotations and translations. However, these methods sample by iterative numerical integration, requiring tens of function evaluations per grasp and limiting their use in real-time manipulation. We propose GraspMeanFlow, an SE(3)-equivariant MeanFlow framework for few-step 6-DoF grasp generation. Our method learns the average velocity over a finite time interval, defined through the time-ordered exponential so that it reproduces exactly the rigid-body displacement accumulated over that interval. We prove that a point-cloud-conditioned distribution transported by an equivariant average-velocity flow map remains invariant, so equivariance is retained under few-step sampling, and we condition the field on a pair of times by lifting both to equivariant vectors, leaving the backbone otherwise unchanged. For stable training, we pair a flow-matching boundary term with either of two consistency terms: the differential MeanFlow identity, whose target requires a Jacobian-vector product, or an equivalent semigroup loss that avoids it. Experiments on ACRONYM show that a single function evaluation of GraspMeanFlow reaches the EMD that an iterative SE(3) flow model needs five steps to approach, that a second instantiation of the same framework improves grasp success by up to 24.3 points in the few-step regime, and that both generate grasp distributions transforming exactly with the object.
J. Kwon, Yikun Bai, Amirhossein Mollaali et al.· 0 citations
This work proposes PartialBiGrasp, a dual-arm grasp generation framework that operates directly on partial point cloud observations that learns geometric features implicitly through convolutional occupancy networks, enabling local reasoning about graspability, collision-free contact regions, and object thickness.
This work proposes a real-world bimanual grasping framework that includes a multimodal dataset capturing joint angles, visual observations and force signals; a Denoising Diffusion Probabilistic Model (DDPM)-based module that generates joint-level grasp configurations from segmented point clouds; and an execution strategy that integrates motion planning with online grasp refinement to ensure physical stability and feasibility.
Ziming Li, Mingxuan Wu, Jiaqi Zhang et al.· 0 citations
Neural-network-based grasp detection has achieved remarkable success in robotic manipulation due to its efficiency and generalization ability. However, detected poses are often not optimized, leading to undesired object motion or collisions during physical execution. This paper proposes a motion-aware refinement framework that minimizes estimated object motion while enforcing collision avoidance. The seven-dimensional pose is decomposed into approach direction, engagement depth, planar projection, and gripper opening width, enabling efficient and interpretable optimization in lower-dimensional subspaces. To evaluate grasp stability beyond conventional success metrics, we introduce the observed success rate (OSR) together with quantitative motion measurements including translation, rotation, and tilt. Real-robot experiments show that, for high-profile objects, the full pipeline improves the measured success rate (MSR) from 93.33% to 100% and OSR from 83.33% to 97.78%. It also reduces the mean translation from <inline-formula> <tex-math notation="LaTeX">$6.099{\,}mm$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$2.684{\,}mm$ </tex-math></inline-formula>, rotation from 3.732° to 1.344°, and tilt from <inline-formula> <tex-math notation="LaTeX">$4.417{\,}mm$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$1.313{\,}mm$ </tex-math></inline-formula>, while requiring <inline-formula> <tex-math notation="LaTeX">$0.82\pm 0.42{\,}s$ </tex-math></inline-formula> on average. For low-profile objects that cannot be detected by the baseline point-cloud-based planner, the full pipeline achieves 100% MSR and OSR.
Tian Tan, Redwan Alqasemi, R. Dubey· IEEE Access· 0 citations
The problem of shaping soft objects is widespread in industrial, medical, and household settings. Hence, robotic Deformable Object Manipulation (DOM) is a field of research that has recently emerged to improve robotic systems’ ability to handle such objects. Indeed, human-robot collaboration is also relevant to applications featuring soft objects, since the decision-making and dexterity of a human operator are currently beyond reach.Our aim is to assess the feasibility of using fast finite element inverse simulation in collaborative shaping tasks. To this end, we propose a computationally efficient method for controlling the shape of an object grasped at both ends. In our experimental setup, a leader robot moves freely along unplanned trajectories, while a controlled robot maintains the desired shape despite these perturbations. We reach an update frequency of 20 Hz for the inverse simulation, with average steady-state shape errors below 10 mm in most cases. Those satisfying results allow us to envision our next milestone: the deployment of the inverse simulation in real human-robot shaping tasks.
A. Koessler, T. Raharijaona, H. Courtecuisse· 2026 IEEE/ASME International...· 0 citations
