Search Results for author: Youngwoon Lee

Found 19 papers, 7 papers with code

HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation

no code implementations15 Mar 2024 Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin, Youngwoon Lee, Pieter Abbeel

Humanoid robots hold great promise in assisting humans in diverse environments and tasks, due to their flexibility and adaptability leveraging human-like morphology.

DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing

no code implementations2 Nov 2023 Vint Lee, Pieter Abbeel, Youngwoon Lee

Model-based reinforcement learning (MBRL) has gained much attention for its ability to learn complex behaviors in a sample-efficient way: planning actions by generating imaginary trajectories with predicted rewards.

Model-based Reinforcement Learning reinforcement-learning

The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning

no code implementations2 Nov 2023 Carmelo Sferrazza, Younggyo Seo, Hao liu, Youngwoon Lee, Pieter Abbeel

For tasks requiring object manipulation, we seamlessly and effectively exploit the complementarity of our senses of vision and touch.

FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation

1 code implementation22 May 2023 Minho Heo, Youngwoon Lee, Doohyun Lee, Joseph J. Lim

We benchmark the performance of offline RL and IL algorithms on our assembly tasks and demonstrate the need to improve such algorithms to be able to solve our tasks in the real world, providing ample opportunities for future research.

Imitation Learning Motion Planning +4

Controllability-Aware Unsupervised Skill Discovery

3 code implementations10 Feb 2023 Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel

One of the key capabilities of intelligent agents is the ability to discover useful skills without external supervision.

PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection

no code implementations9 Dec 2022 Shivin Dass, Karl Pertsch, Hejia Zhang, Youngwoon Lee, Joseph J. Lim, Stefanos Nikolaidis

Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research.

Skill-based Model-based Reinforcement Learning

no code implementations15 Jul 2022 Lucy Xiaoyang Shi, Joseph J. Lim, Youngwoon Lee

From this intuition, we propose a Skill-based Model-based RL framework (SkiMo) that enables planning in the skill space using a skill dynamics model, which directly predicts the skill outcomes, rather than predicting all small details in the intermediate states, step by step.

Model-based Reinforcement Learning reinforcement-learning +1

Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization

no code implementations15 Nov 2021 Youngwoon Lee, Joseph J. Lim, Anima Anandkumar, Yuke Zhu

However, these approaches require larger state distributions to be covered as more policies are sequenced, and thus are limited to short skill sequences.

Reinforcement Learning (RL) Robot Manipulation

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation

1 code implementation11 Nov 2021 I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert, Youngwoon Lee

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 observations.

Imitation Learning Motion Planning +3

Demonstration-Guided Reinforcement Learning with Learned Skills

no code implementations ICLR Workshop SSL-RL 2021 Karl Pertsch, Youngwoon Lee, Yue Wu, Joseph J. Lim

Prior approaches for demonstration-guided RL treat every new task as an independent learning problem and attempt to follow the provided demonstrations step-by-step, akin to a human trying to imitate a completely unseen behavior by following the demonstrator's exact muscle movements.

reinforcement-learning Reinforcement Learning (RL) +1

Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding

1 code implementation1 Jul 2021 Grace Zhang, Linghan Zhong, Youngwoon Lee, Joseph J. Lim

In this paper, we propose a novel policy transfer method with iterative "environment grounding", IDAPT, that alternates between (1) directly minimizing both visual and dynamics domain gaps by grounding the source environment in the target environment domains, and (2) training a policy on the grounded source environment.

Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity

no code implementations1 Jan 2021 Andrew Szot, Youngwoon Lee, Shao-Hua Sun, Joseph J Lim

Humans can effectively learn to estimate how close they are to completing a desired task simply by watching others fulfill the task.

Imitation Learning

Accelerating Reinforcement Learning with Learned Skill Priors

2 code implementations22 Oct 2020 Karl Pertsch, Youngwoon Lee, Joseph J. Lim

We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets.

reinforcement-learning Reinforcement Learning (RL)

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments

no code implementations22 Oct 2020 Jun Yamada, Youngwoon Lee, Gautam Salhotra, Karl Pertsch, Max Pflueger, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert

In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment.

reinforcement-learning Reinforcement Learning (RL) +1

Learning to Coordinate Manipulation Skills via Skill Behavior Diversification

1 code implementation ICLR 2020 Youngwoon Lee, Jingyun Yang, Joseph J. Lim

When mastering a complex manipulation task, humans often decompose the task into sub-skills of their body parts, practice the sub-skills independently, and then execute the sub-skills together.

IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks

1 code implementation17 Nov 2019 Youngwoon Lee, Edward S. Hu, Zhengyu Yang, Alex Yin, Joseph J. Lim

The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks.

Industrial Robots reinforcement-learning +2

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