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

We propose RAPid-Learn: Learning to Recover and Plan Again, a hybrid planning
and learning method, to tackle the problem of adapting to sudden and unexpected
changes in an agent's environment (i.e., novelties). RAPid-Learn is designed to
formulate and solve modifications to a task's Markov Decision Process (MDPs)
on-the-fly and is capable of exploiting domain knowledge to learn any new
dynamics caused by the environmental changes. It is capable of exploiting the
domain knowledge to learn action executors which can be further used to resolve
execution impasses, leading to a successful plan execution. This novelty
information is reflected in its updated domain model. We demonstrate its
efficacy by introducing a wide variety of novelties in a gridworld environment
inspired by Minecraft, and compare our algorithm with transfer learning
baselines from the literature. Our method is (1) effective even in the presence
of multiple novelties, (2) more sample efficient than transfer learning RL
baselines, and (3) robust to incomplete model information, as opposed to pure
symbolic planning approaches.

提出了 RAPid-Learn 算法：一种混合规划和学习的方法，能够在智能体环境中出现突然和意外的变化时，即时解决任务中的 Markov 决策过程修正问题，有效地应对多样的新颖性，并且更加高效、鲁棒、与符号规划方法和传统基于转移学习的强化学习方法相比更加具有优势。

RAPid-Learn: 开放世界环境下学习恢复处理新奇性的框架

RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments

We provide a systematic analysis of levels of integration between discrete
high-level reasoning and continuous low-level reasoning to address hybrid
planning problems in robotics. We identify four distinct strategies for such an
integration: (i) low-level checks are done for all possible cases in advance
and then this information is used during plan generation, (ii) low-level checks
are done exactly when they are needed during the search for a plan, (iii) first
all plans are computed and then infeasible ones are filtered, and (iv) by means
of replanning, after finding a plan, low-level checks identify whether it is
infeasible or not; if it is infeasible, a new plan is computed considering the
results of previous low- level checks. We perform experiments on hybrid
planning problems in robotic manipulation and legged locomotion domains
considering these four methods of integration, as well as some of their
combinations. We analyze the usefulness of levels of integration in these
domains, both from the point of view of computational efficiency (in time and
space) and from the point of view of plan quality relative to its feasibility.
We discuss advantages and disadvantages of each strategy in the light of
experimental results and provide some guidelines on choosing proper strategies
for a given domain.

我们对离散高层推理和连续低层推理之间的集成水平进行了系统分析，以解决机器人领域的混合规划问题，并确定了四种不同的集成策略，我们在机器人操作和腿部运动领域进行了实验以验证这些方法的实用性和优缺点。