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Found 8 papers, 2 papers with code
LORD: Large Models based Opposite Reward Design for Autonomous Driving
Recently, large pretrained models have gained significant attention as zero-shot reward models for tasks specified with desired linguistic goals.
F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving
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.
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception
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.
Generalized Maximum Entropy Reinforcement Learning via Reward Shaping
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.
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approach
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.
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization
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.
Graph Based Multi-layer K-means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces
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.
A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization
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.
