A Sampling-based Motion Planning Framework for Complex Motor Actions
A Sampling-based Motion Planning Framework for Complex Motor Actions
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DOI：
10.1109/iros51168.2021.9636395
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Shlok Sobti;Rahul Shome;Swarat Chaudhuri;L. Kavraki
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文献类型：
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作者：
Shlok Sobti;Rahul Shome;Swarat Chaudhuri;L. Kavraki
我们提出了一个框架，用于规划复杂的运动动作，例如在混乱的现实世界场景中从任意启动状态倾倒或舀取。这些任务的传统方法使用从人类演示中学习的动态运动原语（dmp）。我们增强了最近提出的最先进的DMP技术，能够通过将它们包括在一个新的混合框架中来避障。这是DMP与基于采样的运动规划算法的补充，使用后者来探索场景并到达DMP可以成功完成任务的有希望的区域。实验表明，即使是障碍物感知dmp，在与训练演示在开始、目标和障碍物方面有很大差异的场景中使用时，任务成功率也会受到影响。我们的混合方法通过在模拟中成功完成浇注任务的混乱场景中的任务，显着优于障碍物感知的dmp。我们进一步在一个真实的机器人上演示了我们的方法，用于倒和舀任务。
We present a framework for planning complex motor actions such as pouring or scooping from arbitrary start states in cluttered real-world scenes. Traditional approaches to such tasks use dynamic motion primitives (DMPs) learned from human demonstrations. We enhance a recently proposed state-of-the-art DMP technique capable of obstacle avoidance by including them within a novel hybrid framework. This complements DMPs with sampling-based motion planning algorithms, using the latter to explore the scene and reach promising regions from which a DMP can successfully complete the task. Experiments indicate that even obstacle-aware DMPs suffer in task success when used in scenarios which largely differ from the trained demonstration in terms of the start, goal, and obstacles. Our hybrid approach significantly outperforms obstacle-aware DMPs by successfully completing tasks in cluttered scenes for a pouring task in simulation. We further demonstrate our method on a real robot for pouring and scooping tasks.