no code implementations • 7 Mar 2024 • Ge Yan, Yueh-Hua Wu, Xiaolong Wang
To learn a generalizable multi-task policy with few demonstrations, the pre-training phase of DNAct leverages neural rendering to distill 2D semantic features from foundation models such as Stable Diffusion to a 3D space, which provides a comprehensive semantic understanding regarding the scene.
1 code implementation • 31 Aug 2023 • Yanjie Ze, Ge Yan, Yueh-Hua Wu, Annabella Macaluso, Yuying Ge, Jianglong Ye, Nicklas Hansen, Li Erran Li, Xiaolong Wang
To incorporate semantics in 3D, the reconstruction module utilizes a vision-language foundation model ($\textit{e. g.}$, Stable Diffusion) to distill rich semantic information into the deep 3D voxel.
no code implementations • NeurIPS 2023 • Yueh-Hua Wu, Xiaolong Wang, Masashi Hamaya
This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants.
no code implementations • 5 Apr 2022 • Yueh-Hua Wu, Jiashun Wang, Xiaolong Wang
In this paper, we propose to learn dexterous manipulation using large-scale demonstrations with diverse 3D objects in a category, which are generated from a human grasp affordance model.
1 code implementation • 12 Aug 2021 • Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, Xiaolong Wang
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation.
no code implementations • 19 May 2020 • Yueh-Hua Wu, I-Hau Yeh, David Hu, Hong-Yuan Mark Liao
Specifically, we are required to provide a solution that is able to (1) handle the traffic signal control when certain surveillance cameras that retrieve information for reinforcement learning are down, (2) learn from batch data without a traffic simulator, and (3) make control decisions without shared information across intersections.
Multi-agent Reinforcement Learning reinforcement-learning +1
124 code implementations • 27 Nov 2019 • Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
Ranked #673 on Image Classification on ImageNet
no code implementations • 25 Sep 2019 • Yueh-Hua Wu, Ting-Han Fan, Peter J. Ramadge, Hao Su
Based on the claim, we propose to learn the transition model by matching the distributions of multi-step rollouts sampled from the transition model and the real ones via WGAN.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 29 Jan 2019 • Fan-Yun Sun, Yen-Yu Chang, Yueh-Hua Wu, Shou-De Lin
If artificially intelligent (AI) agents make decisions on behalf of human beings, we would hope they can also follow established regulations while interacting with humans or other AI agents.
no code implementations • 27 Jan 2019 • Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
Imitation learning (IL) aims to learn an optimal policy from demonstrations.
2 code implementations • 6 Sep 2018 • Yen-Yu Chang, Fan-Yun Sun, Yueh-Hua Wu, Shou-De Lin
Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting.
no code implementations • 6 Sep 2018 • Yueh-Hua Wu, Fan-Yun Sun, Yen-Yu Chang, Shou-De Lin
This work provides a thorough study on how reward scaling can affect performance of deep reinforcement learning agents.
1 code implementation • 12 Dec 2017 • Yueh-Hua Wu, Shou-De Lin
This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically.