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SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty
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Generating stable and robust grasps on arbitrary objects is critical for dexterous robotic hands, marking a significant step towards advanced dexterous manipulation. Previous studies have mostly focused on improving differentiable grasping metrics with the assumption of precisely known object geometry. However, shape uncertainty is ubiquitous due to noisy and partial shape observations, which introduce challenges in grasp planning. We propose, SpringGrasp planner, a planner that considers uncertain observations of the object surface for synthesizing compliant dexterous grasps. A compliant dexterous grasp could minimize the effect of unexpected contact with the object, leading to more stable grasp with shape-uncertain objects. We introduce an analytical and differentiable metric, SpringGrasp metric, that evaluates the dynamic behavior of the entire compliant grasping process. Planning with SpringGrasp planner, our method achieves a grasp success rate of 89% from two viewpoints and 84% from a single viewpoints in experiment with a real robot on 14 common objects. Compared with a force-closure based planner, our method achieves at least 18% higher grasp success rate.
Forward citations
Cited by 5 Pith papers
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CoorGrasp: Coordinated Contact Control for Adaptive Dexterous Grasping Under Uncertainty
CoorGrasp's MPC with coordination-aware phase separation, arm-hand adjustment, and adaptive force allocation raises grasp success and cuts in-hand object motion versus open-loop and independent-finger baselines on 15k...
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Current as Touch: Proprioceptive Contact Feedback for Compliant Dexterous Manipulation
Motor current plus joint state predicts compliance reference positions that let standard PD control produce stable, contact-aware grasping without external tactile or force sensors.
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GraspADMM: Improving Dexterous Grasp Synthesis via ADMM Optimization
Decoupling target object contact points from hand contact points in an ADMM loop improves simulated dexterous grasp success by ~15 absolute points over Dexonomy while keeping penetration at zero.
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DexVLG: Dexterous Vision-Language-Grasp Model at Scale
DexVLG is a vision-language model trained on 170 million simulated dexterous grasps that generates hand poses aligned with language instructions about which part of an object to grasp.
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.
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