19 citations · 46 across the 4 of their papers we have counts for
5 papers
Learning to Play by Imitating Humans
Rostam Dinyari, Pierre Sermanet, Corey Lynch
Acquiring multiple skills has commonly involved collecting a large number of expert demonstrations per task or engineering custom reward functions. Recently it has been shown that…
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Abhishek Gupta, Vikash Kumar, Corey Lynch +2
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable,…
Online Object Representations with Contrastive Learning
Sören Pirk, Mohi Khansari, Yunfei Bai +2
We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics.…
Wasserstein Dependency Measure for Representation Learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio +3
Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the-art performance in applications such as…
Learning Latent Plans from Play
Corey Lynch, Mohi Khansari, Ted Xiao +4
Acquiring a diverse repertoire of general-purpose skills remains an open challenge for robotics. In this work, we propose self-supervising control on top of human teleoperated play…