103 citations · 194 across the 16 of their papers we have counts for
17 papers
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Investigation of the mechanism of the anomalous Hall effects in Cr2Te3/(BiSb)2(TeSe)3 heterostructure
Seong Won Cho, In Hak Lee, Youngwoong Lee +8
The interplay between ferromagnetism and the non-trivial topology has unveiled intriguing phases in the transport of charges and spins. For example, it is consistently observed the…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Language-Conditioned Path Planning
Amber Xie, Youngwoon Lee, Pieter Abbeel +1
Contact is at the core of robotic manipulation. At times, it is desired (e.g. manipulation and grasping), and at times, it is harmful (e.g. when avoiding obstacles). However, tradi…
Video Prediction Models as Rewards for Reinforcement Learning
Alejandro Escontrela, Ademi Adeniji, Wilson Yan +6
Specifying reward signals that allow agents to learn complex behaviors is a long-standing challenge in reinforcement learning. A promising approach is to extract preferences for be…
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…
Controllability-Aware Unsupervised Skill Discovery
Seohong Park, Kimin Lee, Youngwoon Lee +1
One of the key capabilities of intelligent agents is the ability to discover useful skills without external supervision. However, the current unsupervised skill discovery methods a…
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
Shivin Dass, Karl Pertsch, Hejia Zhang +3
Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting l…
Skill-based Model-based Reinforcement Learning
Lucy Xiaoyang Shi, Joseph J. Lim, Youngwoon Lee
Model-based reinforcement learning (RL) is a sample-efficient way of learning complex behaviors by leveraging a learned single-step dynamics model to plan actions in imagination. H…
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization
Youngwoon Lee, Joseph J. Lim, Anima Anandkumar +1
Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a polic…
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation
I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme +3
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual…
Demonstration-Guided Reinforcement Learning with Learned Skills
Karl Pertsch, Youngwoon Lee, Yue Wu +1
Demonstration-guided reinforcement learning (RL) is a promising approach for learning complex behaviors by leveraging both reward feedback and a set of target task demonstrations.…
Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding
Grace Zhang, Linghan Zhong, Youngwoon Lee +1
The ability to transfer a policy from one environment to another is a promising avenue for efficient robot learning in realistic settings where task supervision is not available. T…
Accelerating Reinforcement Learning with Learned Skill Priors
Karl Pertsch, Youngwoon Lee, Joseph J. Lim
Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for…
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
Jun Yamada, Youngwoon Lee, Gautam Salhotra +5
Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in envi…
To Follow or not to Follow: Selective Imitation Learning from Observations
Youngwoon Lee, Edward S. Hu, Zhengyu Yang +1
Learning from demonstrations is a useful way to transfer a skill from one agent to another. While most imitation learning methods aim to mimic an expert skill by following the demo…
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks
Youngwoon Lee, Edward S. Hu, Zhengyu Yang +2
The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks. The environment is designed to adv…