Summary of Planrl: a Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning, by Amisha Bhaskar et al.
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning
by Amisha Bhaskar, Zahiruddin Mahammad, Sachin R Jadhav, Pratap Tokekar
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces PLANRL, a framework that combines classical motion planning and reinforcement learning (RL) to improve robotic task execution in real-world scenarios. The approach uses imitation data to bootstrap exploration and dynamically switches between two modes of operation: classical techniques for reaching waypoints and RL for fine-grained manipulation control. The architecture consists of ModeNet for mode classification, NavNet for waypoint prediction, and InteractNet for precise manipulation. By combining the strengths of RL and imitation learning (IL), PLANRL improves sample efficiency and mitigates distribution shift, ensuring robust task execution. The authors evaluate their approach across multiple challenging simulation environments and real-world tasks, demonstrating superior performance in terms of adaptability, efficiency, and generalization compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way for robots to learn how to do tasks by combining two different approaches: following rules and learning from experience. They call it PLANRL, which stands for Planning And Learning Network. The idea is to let the robot decide when to follow rules and when to learn on its own. This helps the robot be more efficient in exploring its environment and better at doing tasks that require fine-tuned control. The team tested their approach on several simulation environments and real-world scenarios, showing it outperformed existing methods. |
Keywords
* Artificial intelligence * Classification * Generalization * Reinforcement learning
