13 citations · 57 across the 11 of their papers we have counts for
12 papers
Neural Randomized Planning for Whole Body Robot Motion
Yunfan Lu, Yuchen Ma, David Hsu +1
Robot motion planning has made vast advances over the past decades, but the challenge remains: robot mobile manipulators struggle to plan long-range whole-body motion in common hou…
What Truly Matters in Trajectory Prediction for Autonomous Driving?
Phong Tran, Haoran Wu, Cunjun Yu +3
Trajectory prediction plays a vital role in the performance of autonomous driving systems, and prediction accuracy, such as average displacement error (ADE) or final displacement e…
The Planner Optimization Problem: Formulations and Frameworks
Yiyuan Lee, Katie Lee, Panpan Cai +2
Identifying internal parameters for planning is crucial to maximizing the performance of a planner. However, automatically tuning internal parameters which are conditioned on the p…
LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty
Mohamad H. Danesh, Panpan Cai, David Hsu
Uncertainty on human behaviors poses a significant challenge to autonomous driving in crowded urban environments. The partially observable Markov decision processes (POMDPs) offer…
Closing the Planning-Learning Loop with Application to Autonomous Driving
Panpan Cai, David Hsu
Real-time planning under uncertainty is critical for robots operating in complex dynamic environments. Consider, for example, an autonomous robot vehicle driving in dense, unregula…
Simulating Autonomous Driving in Massive Mixed Urban Traffic
Yuanfu Luo, Panpan Cai, Yiyuan Lee +1
Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents S…
MAGIC: Learning Macro-Actions for Online POMDP Planning
Yiyuan Lee, Panpan Cai, David Hsu
The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computa…
SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic
Panpan Cai, Yiyuan Lee, Yuanfu Luo +1
Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents S…
GAMMA: A General Agent Motion Model for Autonomous Driving
Yuanfu Luo, Panpan Cai, Yiyuan Lee +1
This paper presents GAMMA, a general motion prediction model that enables large-scale real-time simulation and planning for autonomous driving. GAMMA models heterogeneous, interact…
LeTS-Drive: Driving in a Crowd by Learning from Tree Search
Panpan Cai, Yuanfu Luo, Aseem Saxena +2
Autonomous driving in a crowded environment, e.g., a busy traffic intersection, is an unsolved challenge for robotics. The robot vehicle must contend with a dynamic and partially o…
PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians
Yuanfu Luo, Panpan Cai, Aniket Bera +3
This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is PORCA, a pedestrian motion prediction model that accounts f…
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
Panpan Cai, Yuanfu Luo, David Hsu +1
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algori…