no code implementations • 30 Nov 2023 • Weilian Song, Jieliang Luo, Dale Zhao, Yan Fu, Chin-Yi Cheng, Yasutaka Furukawa
This paper proposes an assistive system for architects that converts a large-scale point cloud into a standardized digital representation of a building for Building Information Modeling (BIM) applications.
no code implementations • 13 Oct 2023 • Harsh Patel, Yuan Zhou, Alexander P Lamb, Shu Wang, Jieliang Luo
By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error.
no code implementations • 29 Sep 2023 • Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari, Jieliang Luo, Pingchuan Ma, Yichen Li, Farhad Javid, Edward Gu, Joshua Jacob, Shinjiro Sueda, Hui Li, Sachin Chitta, Wojciech Matusik
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.
no code implementations • 26 Sep 2023 • Yi Wang, Jieliang Luo, Adam Gaier, Evan Atherton, Hilmar Koch
Concretely, we present a system that leverages Reinforcement Learning (RL) to automatically assign concrete locations on a game map to abstract locations mentioned in a given story (plot facilities), following spatial constraints derived from the story.
no code implementations • 5 Sep 2023 • Md Ferdous Alam, Yi Wang, Linh Tran, Chin-Yi Cheng, Jieliang Luo
We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation.
no code implementations • 20 May 2022 • Yuning Wu, Jieliang Luo, Hui Li
Rewards play an essential role in reinforcement learning.
no code implementations • ICCV 2021 • Kai-Hung Chang, Chin-Yi Cheng, Jieliang Luo, Shingo Murata, Mehdi Nourbakhsh, Yoshito Tsuji
Volumetric design is the first and critical step for professional building design, where architects not only depict the rough 3D geometry of the building but also specify the programs to form a 2D layout on each floor.
1 code implementation • 23 Nov 2020 • Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang, Ye Wang, Pradeep Kumar Jayaraman, Krishna Murthy Jatavallabhula
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
no code implementations • 15 Oct 2020 • Jieliang Luo, Hui Li
In this work we propose a learning approach to high-precision robotic assembly problems.
1 code implementation • 5 Oct 2020 • Karl D. D. Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G. Lambourne, Armando Solar-Lezama, Wojciech Matusik
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software.
no code implementations • 28 Sep 2020 • Karl Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph Lambourne, Armando Solar-Lezama, Wojciech Matusik
We provide a dataset of 8, 625 designs, comprising sequential sketch and extrude modeling operations, together with a complementary environment called the Fusion 360 Gym, to assist with performing CAD reconstruction.
no code implementations • 4 Mar 2020 • Jieliang Luo, Hui Li
Our ablation studies show that Dynamic Experience Replay is a crucial ingredient that either largely shortens the training time in these challenging environments or solves the tasks that the vanilla Ape-X DDPG cannot solve.
no code implementations • 23 Sep 2018 • Yongxiang Fan, Jieliang Luo, Masayoshi Tomizuka
The framework combines both the supervised learning and the reinforcement learning.
no code implementations • 14 Sep 2018 • Jieliang Luo, Sam Green, Peter Feghali, George Legrady, Çetin Kaya Koç
In this paper, we present our extensions of CNN visualization algorithms to the domain of vision-based reinforcement learning.