6 citations · 12 across the 7 of their papers we have counts for
7 papers
BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark
Nikita Chernyadev, Nicholas Backshall, Xiao Ma +3
We introduce BiGym, a new benchmark and learning environment for mobile bi-manual demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home environments, rangin…
Continuous Control with Coarse-to-fine Reinforcement Learning
Younggyo Seo, Jafar Uruç, Stephen James
Despite recent advances in improving the sample-efficiency of reinforcement learning (RL) algorithms, designing an RL algorithm that can be practically deployed in real-world envir…
The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning
Carmelo Sferrazza, Younggyo Seo, Hao Liu +2
Humans rely on the synergy of their senses for most essential tasks. For tasks requiring object manipulation, we seamlessly and effectively exploit the complementarity of our sense…
Guide Your Agent with Adaptive Multimodal Rewards
Changyeon Kim, Younggyo Seo, Hao Liu +4
Developing an agent capable of adapting to unseen environments remains a difficult challenge in imitation learning. This work presents Adaptive Return-conditioned Policy (ARP), an…
Language Reward Modulation for Pretraining Reinforcement Learning
Ademi Adeniji, Amber Xie, Carmelo Sferrazza +3
Using learned reward functions (LRFs) as a means to solve sparse-reward reinforcement learning (RL) tasks has yielded some steady progress in task-complexity through the years. In…
Imitating Graph-Based Planning with Goal-Conditioned Policies
Junsu Kim, Younggyo Seo, Sungsoo Ahn +2
Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the t…