20 citations · 28 across the 4 of their papers we have counts for
4 papers
Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning
Jinning Li, Chen Tang, Masayoshi Tomizuka +1
Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics where exploration is risky and expensive. However, it still struggles to acquire skills…
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
Chenfeng Xu, Tian Li, Chen Tang +5
Deep learning has recently achieved significant progress in trajectory forecasting. However, the scarcity of trajectory data inhibits the data-hungry deep-learning models from lear…
Adaptive Probabilistic Vehicle Trajectory Prediction Through Physically Feasible Bayesian Recurrent Neural Network
Chen Tang, Jianyu Chen, Masayoshi Tomizuka
Probabilistic vehicle trajectory prediction is essential for robust safety of autonomous driving. Current methods for long-term trajectory prediction cannot guarantee the physical…
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
Daniel Seita, David Chan, Roshan Rao +3
Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning. When providing expert demonstrations to human s…