539 citations · 2k across the 41 of their papers we have counts for
3 papers · 1 filter
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua, Roberto Calandra, Rowan McAllister +1
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. T…
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
Roberto Calandra, Andrew Owens, Dinesh Jayaraman +5
For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus can…
Learning Flexible and Reusable Locomotion Primitives for a Microrobot
Brian Yang, Grant Wang, Roberto Calandra +3
The design of gaits for robot locomotion can be a daunting process which requires significant expert knowledge and engineering. This process is even more challenging for robots tha…