69 citations · 153 across the 18 of their papers we have counts for
18 papers
Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)
Tsu-Ching Hsiao, Hao-Wei Chen, Hsuan-Kung Yang +1
Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions. In response, we…
Virtual Guidance as a Mid-level Representation for Navigation with Augmented Reality
Hsuan-Kung Yang, Tsung-Chih Chiang, Jou-Min Liu +4
In the context of autonomous navigation, effectively conveying abstract navigational cues to agents in dynamic environments presents significant challenges, particularly when navig…
Pixel-Wise Prediction based Visual Odometry via Uncertainty Estimation
Hao-Wei Chen, Ting-Hsuan Liao, Hsuan-Kung Yang +1
This paper introduces pixel-wise prediction based visual odometry (PWVO), which is a dense prediction task that evaluates the values of translation and rotation for every pixel in…
CathSim: An Open-source Simulator for Endovascular Intervention
Tudor Jianu, Baoru Huang, Mohamed E. M. K. Abdelaziz +6
Autonomous robots in endovascular operations have the potential to navigate circulatory systems safely and reliably while decreasing the susceptibility to human errors. However, th…
Investigation of Factorized Optical Flows as Mid-Level Representations
Hsuan-Kung Yang, Tsu-Ching Hsiao, Ting-Hsuan Liao +7
In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learnin…
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture
Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting +3
Deep reinforcement learning (DRL) has been demonstrated to provide promising results in several challenging decision making and control tasks. However, the required inference costs…
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation
Chen-Hao Chao, Bo-Wun Cheng, Chun-Yi Lee
Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks. Nevertheless, t…
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
Wei-Fang Sun, Cheng-Kuang Lee, Chun-Yi Lee
In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments are highly stochastic due to the partial observability of each agent and the continuously…
Mixture of Step Returns in Bootstrapped DQN
Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong +1
The concept of utilizing multi-step returns for updating value functions has been adopted in deep reinforcement learning (DRL) for a number of years. Updating value functions with…
Reusability and Transferability of Macro Actions for Reinforcement Learning
Yi-Hsiang Chang, Kuan-Yu Chang, Henry Kuo +1
Conventional reinforcement learning (RL) typically determines an appropriate primitive action at each timestep. However, by using a proper macro action, defined as a sequence of pr…
Flow-based Intrinsic Curiosity Module
Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong +1
In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module…
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
Hsuan-Kung Yang, Po-Han Chiang, Kuan-Wei Ho +2
Exploration bonus derived from the novelty of the states in an environment has become a popular approach to motivate exploration for deep reinforcement learning agents in the past…
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information
Hsuan-Kung Yang, An-Chieh Cheng, Kuan-Wei Ho +2
In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accur…
Adversarial Active Exploration for Inverse Dynamics Model Learning
Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann +2
We present an adversarial active exploration for inverse dynamics model learning, a simple yet effective learning scheme that incentivizes exploration in an environment without any…
Dynamic Video Segmentation Network
Yu-Syuan Xu, Tsu-Jui Fu, Hsuan-Kung Yang +1
In this paper, we present a detailed design of dynamic video segmentation network (DVSNet) for fast and efficient semantic video segmentation. DVSNet consists of two convolutional…
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su +2
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or spars…
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation
Zhang-Wei Hong, Chen Yu-Ming, Shih-Yang Su +10
Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of si…
A Deep Policy Inference Q-Network for Multi-Agent Systems
Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann +2
We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We…