activity
20102022
most citedKeypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning

35 citations · 84 across the 14 of their papers we have counts for

collaborators
Showing cs.ROShow all

19 papers · 1 filter

cs.RO20221 cited

Motion Planning around Obstacles with Convex Optimization

Tobia Marcucci, Mark Petersen, David von Wrangel +1

Trajectory optimization offers mature tools for motion planning in high-dimensional spaces under dynamic constraints. However, when facing complex configuration spaces, cluttered w…

cs.RO20223 cited

Finding and Optimizing Certified, Collision-Free Regions in Configuration Space for Robot Manipulators

Alexandre Amice, Hongkai Dai, Peter Werner +2

Configuration space (C-space) has played a central role in collision-free motion planning, particularly for robot manipulators. While it is possible to check for collisions at a po…

cs.RO2021

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…

cs.RO202112 cited

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.…

cs.RO20212 cited

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…

cs.RO2021

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…