269 citations · 2k across the 61 of their papers we have counts for
5 papers · 1 filter
Learning Dense Rewards for Contact-Rich Manipulation Tasks
Zheng Wu, Wenzhao Lian, Vaibhav Unhelkar +2
Rewards play a crucial role in reinforcement learning. To arrive at the desired policy, the design of a suitable reward function often requires significant domain expertise as well…
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
Todor Davchev, Kevin Sebastian Luck, Michael Burke +3
Manipulation skills involving contact and friction are inherent to many robotics tasks. Using the class of motor primitives for peg-in-hole like insertions, we study how robots can…
An Interior Point Method Solving Motion Planning Problems with Narrow Passages
Jim Mainprice, Nathan Ratliff, Marc Toussaint +1
Algorithmic solutions for the motion planning problem have been investigated for five decades. Since the development of A* in 1969 many approaches have been investigated, tradition…
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Giovanni Sutanto, Katharina Rombach, Yevgen Chebotar +4
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptat…
A New Age of Computing and the Brain
Polina Golland, Jack Gallant, Greg Hager +4
The history of computer science and brain sciences are intertwined. In his unfinished manuscript "The Computer and the Brain," von Neumann debates whether or not the brain can be t…