361 citations · 473 across the 12 of their papers we have counts for
13 papers
CounterAlign: Counterfactual Supervision for Vision-Language-Action Models
Haru Kondoh, Kei Ota, Asako Kanezaki +1
Vision-Language-Action (VLA) models are typically trained with behavior cloning (BC) on expert demonstrations. However, BC provides only positive supervision for expert actions, wi…
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
Yanjie Ze, Ge Yan, Yueh-Hua Wu +6
It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To ach…
Elastic Decision Transformer
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. Although DT purports to generate a…
Learning Generalizable Dexterous Manipulation from Human Grasp Affordance
Yueh-Hua Wu, Jiashun Wang, Xiaolong Wang
Dexterous manipulation with a multi-finger hand is one of the most challenging problems in robotics. While recent progress in imitation learning has largely improved the sample eff…
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu +4
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 manipula…
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization
Yueh-Hua Wu, I-Hau Yeh, David Hu +1
The goal of this work is to provide a viable solution based on reinforcement learning for traffic signal control problems. Although the state-of-the-art reinforcement learning appr…
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh +3
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on co…
Model Imitation for Model-Based Reinforcement Learning
Yueh-Hua Wu, Ting-Han Fan, Peter J. Ramadge +1
Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollout…
Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao +2
Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectivel…
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning
Fan-Yun Sun, Yen-Yu Chang, Yueh-Hua Wu +1
Human behaviors are regularized by a variety of norms or regulations, either to maintain orders or to enhance social welfare. If artificially intelligent (AI) agents make decisions…
ANS: Adaptive Network Scaling for Deep Rectifier Reinforcement Learning Models
Yueh-Hua Wu, Fan-Yun Sun, Yen-Yu Chang +1
This work provides a thorough study on how reward scaling can affect performance of deep reinforcement learning agents. In particular, we would like to answer the question that how…
A Memory-Network Based Solution for Multivariate Time-Series Forecasting
Yen-Yu Chang, Fan-Yun Sun, Yueh-Hua Wu +1
Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns h…
A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents
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. Our model allows the designers of RL a…