PLANRL：一种用于引导强化学习的运动规划与模仿学习框架
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning
AI总结：
PLANRL框架通过动态切换经典运动规划与强化学习，并利用模仿数据引导探索，显著提升了机器人在仿真和真实任务中的样本效率、泛化能力与操作成功率。
摘要：
强化学习（RL）在仿真环境中已取得显著进展，但由于探索和泛化方面的挑战，其在真实世界机器人任务中的应用仍然有限。为解决这些问题，我们提出了PLANRL，这是一个决定机器人何时应使用经典运动规划、何时应学习策略的框架。为了进一步提高探索效率，我们使用模仿数据来引导探索。PLANRL在两种操作模式之间动态切换：当远离物体时使用经典技术到达航点，当即将与物体交互时使用强化学习进行精细操作控制。PLANRL架构由用于模式分类的ModeNet、用于航点预测的NavNet和用于精确操作的InteractNet组成。通过结合强化学习和模仿学习（IL）的优势，PLANRL提高了样本效率并缓解了分布偏移，确保任务执行的鲁棒性。我们在多个具有挑战性的仿真环境和真实世界任务中评估了我们的方法，与现有方法相比，在适应性、效率和泛化方面表现出优越的性能。在仿真中，PLANRL在30k样本时的训练成功率比基线方法高出10-15%，在评估阶段高出30-40%。在真实世界场景中，它在较简单任务上的成功率比基线高出30-40%，并且在复杂的双阶段操作任务中取得了独有的成功。数据集和补充材料可在我们的{https://raaslab.org/projects/NAVINACT/}上找到。
英文摘要：
Reinforcement Learning (RL) has shown remarkable progress in simulation environments, yet its application to real-world robotic tasks remains limited due to challenges in exploration and generalization. To address these issues, we introduce PLANRL, a framework that chooses when the robot should use classical motion planning and when it should learn a policy. To further improve the efficiency in exploration, we use imitation data to bootstrap the exploration. PLANRL dynamically switches between two modes of operation: reaching a waypoint using classical techniques when away from the objects and reinforcement learning for fine-grained manipulation control when about to interact with objects. PLANRL architecture is composed 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. We evaluate our approach across multiple challenging simulation environments and real-world tasks, demonstrating superior performance in terms of adaptability, efficiency, and generalization compared to existing methods. In simulations, PLANRL surpasses baseline methods by 10-15\% in training success rates at 30k samples and by 30-40\% during evaluation phases. In real-world scenarios, it demonstrates a 30-40\% higher success rate on simpler tasks compared to baselines and uniquely succeeds in complex, two-stage manipulation tasks. Datasets and supplementary materials can be found on our {https://raaslab.org/projects/NAVINACT/}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
