most citedThinking While Moving: Deep Reinforcement Learning with Concurrent Control

9 citations · 9 across the 1 of their papers we have counts for

collaborators

2 papers

cs.LG2020★ 9 cited

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

Ted Xiao, Eric Jang, Dmitry Kalashnikov +4

We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robo…

cs.RO2018

Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks

Stephen James, Paul Wohlhart, Mrinal Kalakrishnan +6

Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amou…