327 citations · 1.3k across the 66 of their papers we have counts for
7 papers · 1 filter
SEED: Series Elastic End Effectors in 6D for Visuotactile Tool Use
H. J. Terry Suh, Naveen Kuppuswamy, Tao Pang +3
We propose the framework of Series Elastic End Effectors in 6D (SEED), which combines a spatially compliant element with visuotactile sensing to grasp and manipulate tools in the w…
Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning
Danny Driess, Jung-Su Ha, Marc Toussaint +1
This work proposes an optimization-based manipulation planning framework where the objectives are learned functionals of signed-distance fields that represent objects in the scene.…
Lyapunov-stable neural-network control
Hongkai Dai, Benoit Landry, Lujie Yang +2
Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement learning algorithms have been highly effective in synthesizing neural-network controllers…
Bundled Gradients through Contact via Randomized Smoothing
H. J. Terry Suh, Tao Pang, Russ Tedrake
The empirical success of derivative-free methods in reinforcement learning for planning through contact seems at odds with the perceived fragility of classical gradient-based optim…
Variable compliance and geometry regulation of Soft-Bubble grippers with active pressure control
Sihah Joonhigh, Naveen Kuppuswamy, Andrew Beaulieu +2
While compliant grippers have become increasingly commonplace in robot manipulation, finding the right stiffness and geometry for grasping the widest variety of objects remains a k…
kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation
Wei Gao, Russ Tedrake
In this paper, we explore generalizable, perception-to-action robotic manipulation for precise, contact-rich tasks. In particular, we contribute a framework for closed-loop robotic…
Shortest Paths in Graphs of Convex Sets
Tobia Marcucci, Jack Umenberger, Pablo A. Parrilo +1
Given a graph, the shortest-path problem requires finding a sequence of edges with minimum cumulative length that connects a source vertex to a target vertex. We consider a variant…