Automatic Cross-domain Task Plan Transfer by Caching Abstract Skills
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-21090-7_28
Reference17 articles.
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3. Chamzas, C., Kingston, Z., Quintero-Peña, C., Shrivastava, Kavraki, L.E.: Learning sampling distributions using local 3D Workspace Decompositions for Motion Planning in High Dimensions. In: IEEE International Conference on Robotics and Automation, pp. 1283–1289, June 2021
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Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Accelerating Long-Horizon Planning with Affordance-Directed Dynamic Grounding of Abstract Strategies;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
2. Extracting generalizable skills from a single plan execution using abstraction-critical state detection;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29
3. Efficient Task Planning Using Abstract Skills and Dynamic Road Map Matching;Springer Proceedings in Advanced Robotics;2023
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