132 citations · 543 across the 32 of their papers we have counts for
3 papers · 1 filter
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…
Task-Embedded Control Networks for Few-Shot Imitation Learning
Stephen James, Michael Bloesch, Andrew J. Davison
Much like humans, robots should have the ability to leverage knowledge from previously learned tasks in order to learn new tasks quickly in new and unfamiliar environments. Despite…
Sim-to-Real Reinforcement Learning for Deformable Object Manipulation
Jan Matas, Stephen James, Andrew J. Davison
We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformabl…