Algorithmic Foundations of Robotics XIII - Proceedings of the 13th Workshop on the Algorithmic Foundations of Robotics
Algorithmic Foundations of Robotics XIII - Proceedings of the 13th Workshop on the Algorithmic Foundations of Robotics
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机器人算法基础 XIII - 第 13 届机器人算法基础研讨会论文集
DOI：
10.1007/978-3-030-44051-0_10
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Agboh W
中科院分区：
文献类型：
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作者：
Agboh W
我们提出了一种基于物理的操作的计划和控制方法。该算法的关键特点是它可以适应任务的精度要求，当任务要求高精度时，它会减速并产生“小心”的运动，而当任务允许不精确时，它会加速并快速移动。我们将该问题表述为具有动作依赖随机性的MDP，并提出了它的近似在线解。我们使用具有确定性模型的轨迹优化器向MDP建议有希望的动作，以减少评估不同动作所花费的计算时间。我们在模拟和真实的机器人系统上进行了实验。我们的研究结果表明，通过任务自适应规划和控制方法，机器人可以根据任务的精度和不确定性水平选择快速或缓慢的动作。机器人可以在线做出这些决定，并且能够在尽可能快地完成操作任务的同时保持较高的成功率。
We propose a planning and control approach to physics-based manipulation. The key feature of the algorithm is that it can adapt to the accuracy requirements of a task, by slowing down and generating “careful” motion when the task requires high accuracy, and by speeding up and moving fast when the task tolerates inaccuracy. We formulate the problem as an MDP with action-dependent stochasticity and propose an approximate online solution to it. We use a trajectory optimizer with a deterministic model to suggest promising actions to the MDP, to reduce computation time spent on evaluating different actions. We conducted experiments in simulation and on a real robotic system. Our results show that with a task-adaptive planning and control approach, a robot can choose fast or slow actions depending on the task accuracy and uncertainty level. The robot makes these decisions online and is able to maintain high success rates while completing manipulation tasks as fast as possible.