no code implementations • 27 Mar 2024 • Xin Ye, Feng Tao, Abhirup Mallik, Burhaneddin Yaman, Liu Ren
Recently, large pretrained models have gained significant attention as zero-shot reward models for tasks specified with desired linguistic goals.
1 code implementation • 7 Mar 2023 • Ekta U. Samani, Feng Tao, Harshavardhan R. Dasari, Sihao Ding, Ashis G. Banerjee
We take the first step in addressing this challenge and introduce a baseline, F2BEV, to generate discretized BEV height maps and BEV semantic segmentation maps from fisheye images.
no code implementations • 15 Oct 2021 • Yiming Cui, Zhiwen Cao, Yixin Xie, Xingyu Jiang, Feng Tao, Yingjie Chen, Lin Li, Dongfang Liu
The existing MOTS studies face two critical challenges: 1) the published datasets inadequately capture the real-world complexity for network training to address various driving settings; 2) the working pipeline annotation tool is under-studied in the literature to improve the quality of MOTS learning examples.
no code implementations • 29 Sep 2021 • Feng Tao, Yongcan Cao
We also show the addition of the agent’s policy entropy at the next state yields new soft Q function and state value function that are concise and modular.
1 code implementation • 15 Oct 2020 • Huixin Zhan, Feng Tao, Yongcan Cao
To reduce and minimize the need for human queries, we propose a new GAN-assisted human preference-based reinforcement learning approach that uses a generative adversarial network (GAN) to actively learn human preferences and then replace the role of human in assigning preferences.
no code implementations • 21 Sep 2020 • Feng Tao, Yongcan Cao
In this paper, we study the problem of obtaining a control policy that can mimic and then outperform expert demonstrations in Markov decision processes where the reward function is unknown to the learning agent.
no code implementations • 21 Sep 2020 • Feng Tao, Rengan Suresh, Johnathan Votion, Yongcan Cao
Based on the dual graph and graph theory, we then generalize MLKM to G-MLKM by first understanding local data-target association and then extracting cross-local data-target association mathematically analyze the data association at intersections of that space.
no code implementations • 30 May 2017 • Samuel Silva, Rengan Suresh, Feng Tao, Johnathan Votion, Yongcan Cao
Data-target association is an important step in multi-target localization for the intelligent operation of un- manned systems in numerous applications such as search and rescue, traffic management and surveillance.