16 citations · 42 across the 4 of their papers we have counts for
4 papers · 1 filter
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer
Rae Jeong, Jackie Kay, Francesco Romano +6
Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system. On the other hand, sim…
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
Rae Jeong, Yusuf Aytar, David Khosid +5
Collecting and automatically obtaining reward signals from real robotic visual data for the purposes of training reinforcement learning algorithms can be quite challenging and time…
Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov +13
We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show h…
Robust Reinforcement Learning for Continuous Control with Model Misspecification
Daniel J. Mankowitz, Nir Levine, Rae Jeong +7
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcem…