3 papers
cs.LG2024
Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration
Dongyoung Kim, Jinwoo Shin, Pieter Abbeel +1
A promising technique for exploration is to maximize the entropy of visited state distribution, i.e., state entropy, by encouraging uniform coverage of visited state space. While i…
cs.RO2024
GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators
Philipp Wu, Yide Shentu, Zhongke Yi +2
Humans can teleoperate robots to accomplish complex manipulation tasks. Imitation learning has emerged as a powerful framework that leverages human teleoperated demonstrations to t…
cs.CL2024
Learning to Model the World with Language
Jessy Lin, Yuqing Du, Olivia Watkins +4
To interact with humans and act in the world, agents need to understand the range of language that people use and relate it to the visual world. While current agents can learn to e…