When reasoning about actions, e.g., by means of task planning or agent
programming with Golog, the robot's actions are typically modeled on an
abstract level, where complex actions such as picking up an object are treated
as atomic primitives with deterministic effects and preconditions that only
depend on the current state. However, when executing such an action on a robot
it can no longer be seen as a primitive. Instead, action execution is a complex
task involving multiple steps with additional temporal preconditions and timing
constraints. Furthermore, the action may be noisy, e.g., producing erroneous
sensing results and not always having the desired effects. While these aspects
are typically ignored in reasoning tasks, they need to be dealt with during
execution. In this thesis, we propose several approaches towards closing this
gap.

当对行为进行推理时，如通过任务规划或使用 Golog 进行机器人代理编程时，机器人的行为通常建模在抽象层次上，其中复杂的动作（例如拾取对象）被视为具有确定性效果和仅依赖于当前状态的前提条件的原子原语。然而，当在机器人上执行此类动作时，它不再被视为原始动作，而是涉及多个步骤、具有额外时态前提条件和时间约束的复杂任务。此外，该行为可能出现噪声，例如产生错误感知结果并且并非总是具有期望的效果。虽然这些方面通常被忽略在推理任务中，但在执行期间需要处理它们。在这篇论文中，我们提出了几种方法来填补这个差距。

走向在机器人上高层次推理与执行之间的桥梁

Towards Bridging the Gap between High-Level Reasoning and Execution on  Robots

Some actions must be executed in different ways depending on the context. For
example, wiping away marker requires vigorous force while wiping away almonds
requires more gentle force. In this paper we provide a model where an agent
learns which manner of action execution to use in which context, drawing on
evidence from trial and error and verbal corrections when it makes a mistake
(e.g., ``no, gently''). The learner starts out with a domain model that lacks
the concepts denoted by the words in the teacher's feedback; both the words
describing the context (e.g., marker) and the adverbs like ``gently''. We show
that through the the semantics of coherence, our agent can perform the symbol
grounding that's necessary for exploiting the teacher's feedback so as to solve
its domain-level planning problem: to perform its actions in the current
context in the right way.

该论文介绍了一种代理学习模型，在不同的上下文中学习行动执行方式，并利用试错和口头纠正来解决其领域级计划问题。