2 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Robust Adaptive Safe Robotic Grasping with Tactile Sensing
Yitaek Kim, Jeeseop Kim, Albert H. Li +2
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 framewo…
Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer
Tyler Ga Wei Lum, Albert H. Li, Preston Culbertson +4
This work explores conditions under which multi-finger grasping algorithms can attain robust sim-to-real transfer. While numerous large datasets facilitate learning generative mode…
DROP: Dexterous Reorientation via Online Planning
Albert H. Li, Preston Culbertson, Vince Kurtz +1
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 (R…
Toward An Analytic Theory of Intrinsic Robustness for Dexterous Grasping
Albert H. Li, Preston Culbertson, Aaron D. Ames
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 plann…
Bounding Stochastic Safety: Leveraging Freedman's Inequality with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Preston Culbertson, Aaron D. Ames
When deployed in the real world, safe control methods must be robust to unstructured uncertainties such as modeling error and external disturbances. Typical robust safety methods a…