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Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framework for grasping with formal safety guarantees based on Control Barrier
Externí odkaz:
http://arxiv.org/abs/2411.07833
Autor:
Lum, Tyler Ga Wei, Li, Albert H., Culbertson, Preston, Srinivasan, Krishnan, Ames, Aaron D., Schwager, Mac, Bohg, Jeannette
This work explores conditions under which multi-finger grasping algorithms can attain robust sim-to-real transfer. While numerous large datasets facilitate learning generative models for multi-finger grasping at scale, reliable real-world dexterous g
Externí odkaz:
http://arxiv.org/abs/2410.23701
Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (RL), one popular approach has been to use massively-parallelized, domai
Externí odkaz:
http://arxiv.org/abs/2409.14562
Conventional approaches to grasp planning require perfect knowledge of an object's pose and geometry. Uncertainties in these quantities induce uncertainties in the quality of planned grasps, which can lead to failure. Classically, grasp robustness re
Externí odkaz:
http://arxiv.org/abs/2403.07249
Classical approaches to grasp planning are deterministic, requiring perfect knowledge of an object's pose and geometry. In response, data-driven approaches have emerged that plan grasps entirely from sensory data. While these data-driven methods have
Externí odkaz:
http://arxiv.org/abs/2309.16930
Many approaches to grasp synthesis optimize analytic quality metrics that measure grasp robustness based on finger placements and local surface geometry. However, generating feasible dexterous grasps by optimizing these metrics is slow, often taking
Externí odkaz:
http://arxiv.org/abs/2302.13687
Autor:
Saha, Seemanta, Sarker, Laboni, Shafiuzzaman, Md, Shou, Chaofan, Li, Albert, Sankaran, Ganesh, Bultan, Tevfik
Starting with a random initial seed, fuzzers search for inputs that trigger bugs or vulnerabilities. However, fuzzers often fail to generate inputs for program paths guarded by restrictive branch conditions. In this paper, we show that by first ident
Externí odkaz:
http://arxiv.org/abs/2212.09004
Autor:
Chan, Hei-Long, Yuen, Hoi-Man, Au, Chun-Ting, Chan, Kate Ching-Ching, Li, Albert Martin, Lui, Lok-Ming
Publikováno v:
In Pattern Recognition August 2024 152
