87 citations · 196 across the 11 of their papers we have counts for
12 papers
Primitive Skill-based Robot Learning from Human Evaluative Feedback
Ayano Hiranaka, Minjune Hwang, Sharon Lee +4
Reinforcement learning (RL) algorithms face significant challenges when dealing with long-horizon robot manipulation tasks in real-world environments due to sample inefficiency and…
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
Wenlong Huang, Chen Wang, Ruohan Zhang +3
Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the p…
MimicPlay: Long-Horizon Imitation Learning by Watching Human Play
Chen Wang, Linxi Fan, Jiankai Sun +5
Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world. However, learning complex long-horizon tasks often r…
Interaction Modeling with Multiplex Attention
Fan-Yun Sun, Isaac Kauvar, Ruohan Zhang +4
Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that…
Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks
Ruohan Zhang, Faraz Torabi, Garrett Warnell +1
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is…
Machine versus Human Attention in Deep Reinforcement Learning Tasks
Sihang Guo, Ruohan Zhang, Bo Liu +4
Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks. However, the trained models are often difficult to interpret, because they are…