34 citations · 48 across the 16 of their papers we have counts for
8 papers · 1 filter
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
Saad Abdul Ghani, Zizhao Wang, Peter Stone +1
This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic…
Learning to Correct Mistakes: Backjumping in Long-Horizon Task and Motion Planning
Yoonchang Sung, Zizhao Wang, Peter Stone
As robots become increasingly capable of manipulation and long-term autonomy, long-horizon task and motion planning problems are becoming increasingly important. A key challenge in…
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022
Xuesu Xiao, Zifan Xu, Zizhao Wang +14
The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022) in Philadelphia, PA. The aim…
APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback
Zizhao Wang, Xuesu Xiao, Garrett Warnell +1
Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however,…
From Agile Ground to Aerial Navigation: Learning from Learned Hallucination
Zizhao Wang, Xuesu Xiao, Alexander J Nettekoven +5
This paper presents a self-supervised Learning from Learned Hallucination (LfLH) method to learn fast and reactive motion planners for ground and aerial robots to navigate through…
APPL: Adaptive Planner Parameter Learning
Xuesu Xiao, Zizhao Wang, Zifan Xu +5
While current autonomous navigation systems allow robots to successfully drive themselves from one point to another in specific environments, they typically require extensive manua…
APPLI: Adaptive Planner Parameter Learning From Interventions
Zizhao Wang, Xuesu Xiao, Bo Liu +2
While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptima…
APPLR: Adaptive Planner Parameter Learning from Reinforcement
Zifan Xu, Gauraang Dhamankar, Anirudh Nair +5
Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-t…