153 citations · 385 across the 25 of their papers we have counts for
7 papers · 1 filter
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…
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning
Eddy Hudson, Ishan Durugkar, Garrett Warnell +1
Given a dataset of expert agent interactions with an environment of interest, a viable method to extract an effective agent policy is to estimate the maximum likelihood policy indi…
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence
Peter Stone, Rodney Brooks, Erik Brynjolfsson +14
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial…
Learning Real-world Autonomous Navigation by Self-Supervised Environment Synthesis
Zifan Xu, Anirudh Nair, Xuesu Xiao +1
Machine learning approaches have recently enabled autonomous navigation for mobile robots in a data-driven manner. Since most existing learning-based navigation systems are trained…
Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways
Jin-Soo Park, Xuesu Xiao, Garrett Warnell +2
While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple r…
Visually Grounded Task and Motion Planning for Mobile Manipulation
Xiaohan Zhang, Yifeng Zhu, Yan Ding +3
Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals, while maintaining motion-level feasibility. This paper focuses on TAMP domains that involve…