163 citations · 172 across the 4 of their papers we have counts for
4 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…
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation
Minho Heo, Youngwoon Lee, Doohyun Lee +1
Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks. Howeve…
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…
Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve +4
Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency i.e., the model requires several (…