Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 7, Issue 4, Pages 12283-12290Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3214784
Keywords
Task planning; reactive and sensor-based planning
Categories
Funding
- National Natural Science Foundation of China [U20A20334, U19B2019, M-0248]
- Tsinghua-Meituan Joint Institute for Digital Life
- Tsinghua EE Independent Research Project
- Beijing National Research Center for Information Science and Technology (BNRist)
- Beijing Innovation Center for Future Chips
Ask authors/readers for more resources
This letter presents a framework to co-optimize robot exploration and task planning in unknown environments. A unified structure called subtask is designed to decompose the exploration and planning phases, and a value function and value-based scheduler are developed to select the appropriate subtask each time. The framework is evaluated in a photo-realistic simulator, achieving a 25%-29% increase in task efficiency.
Robots often need to accomplish complex tasks in unknown environments, which is a challenging problem, involving autonomous exploration for acquiring necessary scene knowledge and task planning. In traditional approaches, the agent first explores the environment to instantiate a complete planning domain and then invokes a symbolic planner to plan and perform high-level actions. However, task execution is inefficient since the two processes involve many repetitive states and actions. Hence, this letter proposes a framework to co-optimize robot exploration and task planning in unknown environments. To afford robot exploration and symbolic planning not being independent and separated, we design a unified structure named subtask, which is exploited to decompose the robot exploration and planning phases. To select the appropriate subtask each time, we develop a value function and a value-based scheduler to co-optimize exploration and task processing. Our framework is evaluated in a photo-realistic simulator with three complex household tasks, increasing task efficiency by 25%-29%.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available