384 citations · 807 across the 37 of their papers we have counts for
3 papers · 2 filters
Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions
Lerrel Pinto, Aditya Mandalika, Brian Hou +1
This paper proposes a sample-efficient yet simple approach to learning closed-loop policies for nonprehensile manipulation. Although reinforcement learning (RL) can learn closed-lo…
Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation
Pratyusha Sharma, Lekha Mohan, Lerrel Pinto +1
In recent years, we have seen an emergence of data-driven approaches in robotics. However, most existing efforts and datasets are either in simulation or focus on a single task in…
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
Abhinav Gupta, Adithyavairavan Murali, Dhiraj Gandhi +1
Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in cura…