Task and motion planning represents a powerful set of hybrid planning methods
that combine reasoning over discrete task domains and continuous motion
generation. Traditional reasoning necessitates task domain models and enough
information to ground actions to motion planning queries. Gaps in this
knowledge often arise from sources like occlusion or imprecise modeling. This
work generates task and motion plans that include actions cannot be fully
grounded at planning time. During execution, such an action is handled by a
provided human-designed or learned closed-loop behavior. Execution combines
offline planned motions and online behaviors till reaching the task goal.
Failures of behaviors are fed back as constraints to find new plans. Forty
real-robot trials and motivating demonstrations are performed to evaluate the
proposed framework and compare against state-of-the-art. Results show faster
execution time, less number of actions, and more success in problems where
diverse gaps arise. The experiment data is shared for researchers to simulate
these settings. The work shows promise in expanding the applicable class of
realistic partially grounded problems that robots can address.

该研究通过组合对离散任务领域和连续运动生成进行推理的混合规划方法，生成包含无法完全基于计划时间进行植入的行为的任务和运动计划，在执行过程中使用由人设计或学习的闭环行为处理此类行为，并通过反馈行为故障作为约束条件找到新的计划，从而在真实机器人试验中展示了更快的执行时间、更少的动作数量以及在出现多样化间隙问题时更高的成功率，并与最新技术进行了比较，提供实验数据供研究人员模拟这些情景，为机器人解决更多现实部分植入问题的适用类别展示了希望。

实际执行中的任务和动作规划

Task and Motion Planning for Execution in the Real

This paper presents a learning-based control strategy for non-linear throttle
valves with an asymmetric hysteresis, leading to a near-optimal controller
without requiring any prior knowledge about the environment. We start with a
carefully tuned Proportional Integrator (PI) controller and exploit the recent
advances in Reinforcement Learning (RL) with Guides to improve the closed-loop
behavior by learning from the additional interactions with the valve. We test
the proposed control method in various scenarios on three different valves, all
highlighting the benefits of combining both PI and RL frameworks to improve
control performance in non-linear stochastic systems. In all the experimental
test cases, the resulting agent has a better sample efficiency than traditional
RL agents and outperforms the PI controller.

本文提出了一种基于学习的控制策略，用于具有非对称滞后的非线性节流阀，从而实现接近最优的控制器，不需要任何关于环境的先验知识。通过仔细调整的比例积分器（PI）控制器并利用强化学习（RL）在引导策略上的最新进展，通过与阀门的额外交互学习改进闭环行为。我们在三个不同的阀门上的各种情况下测试了所提出的控制方法，所有情况都突出了结合 PI 和 RL 框架以改善非线性随机系统中的控制性能的好处。在所有实验测试用例中，所得到的代理比传统 RL 代理具有更好的样本效率，并且优于 PI 控制器。