Dyna-LfLH：从学习到的幻觉中学习动态环境下的敏捷导航
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
AI总结：
针对动态密集障碍物下导航的经典规划器算力受限、学习类规划器缺优质演示或探索低效问题，提出自监督方法Dyna-LfLH，基于LfH框架用学习到的潜在分布生成动态障碍物合成训练数据，在地面机器人仿真与真实实验中成功率最高比基线提升25%。
摘要：
本文提出了动态从学习幻觉中学习（Dynamic Learning from Learned Hallucination, Dyna-LfLH），这是一种用于训练运动规划器在存在密集动态障碍物的环境中导航的自监督方法。经典规划器因计算能力有限，难以应对密集且不可预测的障碍物；而基于学习的规划器则面临着为模仿学习获取高质量演示数据，或解决强化学习中探索效率低下的难题。从幻觉中学习（Learning from Hallucination, LfH）方法可从更简单环境中过往成功的导航经验合成训练数据，Dyna-LfLH在其基础上，通过学习到的潜在分布生成动态障碍物，将动态障碍物纳入训练流程，从而实现高效且安全的运动规划器训练。我们在地面机器人上针对模拟环境和真实环境开展了Dyna-LfLH的评估，结果显示其成功率较基线方法最高提升25%。
英文摘要：
This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic obstacles. Classical planners struggle with dense, unpredictable obstacles due to limited computation, while learning-based planners face challenges in acquiring high-quality demonstrations for imitation learning or dealing with exploration inefficiencies in reinforcement learning. Building on Learning from Hallucination (LfH), which synthesizes training data from past successful navigation experiences in simpler environments, Dyna-LfLH incorporates dynamic obstacles by generating them through a learned latent distribution. This enables efficient and safe motion planner training. We evaluate Dyna-LfLH on a ground robot in both simulated and real environments, achieving up to a 25% improvement in success rate compared to baselines.
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。
