15 citations · 16 across the 2 of their papers we have counts for
2 papers
cs.RO2020★ 15 cited
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
Jun Yamada, Youngwoon Lee, Gautam Salhotra +5
Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in envi…
cs.LG2020★ 1 cited
Plan-Space State Embeddings for Improved Reinforcement Learning
Max Pflueger, Gaurav S. Sukhatme
Robot control problems are often structured with a policy function that maps state values into control values, but in many dynamic problems the observed state can have a difficult…