72 citations · 108 across the 9 of their papers we have counts for
9 papers
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Jesse Zhang, Jiahui Zhang, Karl Pertsch +5
We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior w…
Learning to Act from Actionless Videos through Dense Correspondences
Po-Chen Ko, Jiayuan Mao, Yilun Du +2
In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video dem…
Hierarchical Neural Program Synthesis
Linghan Zhong, Ryan Lindeborg, Jesse Zhang +2
Program synthesis aims to automatically construct human-readable programs that satisfy given task specifications, such as input/output pairs or demonstrations. Recent works have de…
Diffusion Model-Augmented Behavioral Cloning
Shang-Fu Chen, Hsiang-Chun Wang, Ming-Hao Hsu +2
Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from environments. Most existing imitation learning…
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing
Grace Zhang, Ayush Jain, Injune Hwang +2
Multi-task reinforcement learning (MTRL) aims to learn several tasks simultaneously for better sample efficiency than learning them separately. Traditional methods achieve this by…
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
Guan-Ting Liu, En-Pei Hu, Pu-Jen Cheng +2
Aiming to produce reinforcement learning (RL) policies that are human-interpretable and can generalize better to novel scenarios, Trivedi et al. (2021) present a method (LEAPS) tha…