57 citations · 118 across the 13 of their papers we have counts for
3 papers · 1 filter
GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot
Tianli Ding, Laura Graesser, Saminda Abeyruwan +5
Learning goal conditioned control in the real world is a challenging open problem in robotics. Reinforcement learning systems have the potential to learn autonomously via trial-and…
i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops
Saminda Abeyruwan, Laura Graesser, David B. D'Ambrosio +6
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to train policies in simulation enables safe exploration and large-scale data collection…
The State of Sparse Training in Deep Reinforcement Learning
Laura Graesser, Utku Evci, Erich Elsen +1
The use of sparse neural networks has seen rapid growth in recent years, particularly in computer vision. Their appeal stems largely from the reduced number of parameters required…