no code implementations • 17 Sep 2023 • Hsuan-Kung Yang, Tsung-Chih Chiang, Ting-Ru Liu, Chun-Wei Huang, Jou-Min Liu, Chun-Yi Lee
The challenge of navigation in environments with dynamic objects continues to be a central issue in the study of autonomous agents.
no code implementations • 25 May 2023 • Tsu-Ching Hsiao, Hao-Wei Chen, Hsuan-Kung Yang, Chun-Yi Lee
Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions.
no code implementations • 5 Mar 2023 • Hsuan-Kung Yang, Tsung-Chih Chiang, Ting-Ru Liu, Chun-Wei Huang, Jou-Min Liu, Chun-Yi Lee
In the context of autonomous navigation, effectively conveying abstract navigational cues to agents in dynamic environments poses challenges, particularly when the navigation information is multimodal.
no code implementations • CVPR 2023 • Lijin Yang, Quan Kong, Hsuan-Kung Yang, Wadim Kehl, Yoichi Sato, Norimasa Kobori
Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding.
no code implementations • 18 Aug 2022 • Hao-Wei Chen, Ting-Hsuan Liao, Hsuan-Kung Yang, Chun-Yi Lee
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 its input observations.
no code implementations • 9 Mar 2022 • Hsuan-Kung Yang, Tsu-Ching Hsiao, Ting-Hsuan Liao, Hsu-Shen Liu, Li-Yuan Tsao, Tzu-Wen Wang, Shan-Ya Yang, Yu-Wen Chen, Huang-Ru Liao, Chun-Yi Lee
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 learning based robotic frameworks.
no code implementations • 16 Jul 2020 • Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong, Chun-Yi Lee
Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets.
1 code implementation • 24 May 2019 • Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong, Chun-Yi Lee
Exploration bonuses derived from the novelty of observations in an environment have become a popular approach to motivate exploration for reinforcement learning (RL) agents in the past few years.
no code implementations • 24 Jan 2019 • Hsuan-Kung Yang, Po-Han Chiang, Kuan-Wei Ho, Min-Fong Hong, Chun-Yi Lee
We propose to employ optical flow estimation errors to examine the novelty of new observations, such that agents are able to memorize and understand the visited states in a more comprehensive fashion.
no code implementations • 9 Sep 2018 • Hsuan-Kung Yang, An-Chieh Cheng, Kuan-Wei Ho, Tsu-Jui Fu, Chun-Yi Lee
The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver.
no code implementations • CVPR 2018 • Yu-Syuan Xu, Tsu-Jui Fu, Hsuan-Kung Yang, Chun-Yi Lee
We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score.
no code implementations • 1 Feb 2018 • Zhang-Wei Hong, Chen Yu-Ming, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Hsuan-Kung Yang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Yueh-Chuan Chang, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Chun-Yi Lee
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 simulators as the training platform.