Integrated task and motion planning (TAMP) has proven to be a valuable
approach to generalizable long-horizon robotic manipulation and navigation
problems. However, the typical TAMP problem formulation assumes full
observability and deterministic action effects. These assumptions limit the
ability of the planner to gather information and make decisions that are
risk-aware. We propose a strategy for TAMP with Uncertainty and Risk Awareness
(TAMPURA) that is capable of efficiently solving long-horizon planning problems
with initial-state and action outcome uncertainty, including problems that
require information gathering and avoiding undesirable and irreversible
outcomes. Our planner reasons under uncertainty at both the abstract task level
and continuous controller level. Given a set of closed-loop goal-conditioned
controllers operating in the primitive action space and a description of their
preconditions and potential capabilities, we learn a high-level abstraction
that can be solved efficiently and then refined to continuous actions for
execution. We demonstrate our approach on several robotics problems where
uncertainty is a crucial factor and show that reasoning under uncertainty in
these problems outperforms previously proposed determinized planning, direct
search, and reinforcement learning strategies. Lastly, we demonstrate our
planner on two real-world robotics problems using recent advancements in
probabilistic perception.

这篇研究论文提出了一种具有不确定性和风险意识的综合任务与动作规划（TAMPURA）策略，能够高效解决具有初始状态和动作结果不确定性的长时程规划问题，通过在抽象任务层和连续控制器层面上进行不确定性推理，该方法在面临不确定性的机器人问题上表现出色。

带有不确定性和风险意识的部分可观测任务与动作规划

Partially Observable Task and Motion Planning with Uncertainty and Risk  Awareness

Imitation learning practitioners have often noted that conditioning policies
on previous actions leads to a dramatic divergence between "held out" error and
performance of the learner in situ. Interactive approaches can provably address
this divergence but require repeated querying of a demonstrator. Recent work
identifies this divergence as stemming from a "causal confound" in predicting
the current action, and seek to ablate causal aspects of current state using
tools from causal inference. In this work, we argue instead that this
divergence is simply another manifestation of covariate shift, exacerbated
particularly by settings of feedback between decisions and input features. The
learner often comes to rely on features that are strongly predictive of
decisions, but are subject to strong covariate shift.
Our work demonstrates a broad class of problems where this shift can be
mitigated, both theoretically and practically, by taking advantage of a
simulator but without any further querying of expert demonstration. We analyze
existing benchmarks used to test imitation learning approaches and find that
these benchmarks are realizable and simple and thus insufficient for capturing
the harder regimes of error compounding seen in real-world decision making
problems. We find, in a surprising contrast with previous literature, but
consistent with our theory, that naive behavioral cloning provides excellent
results. We detail the need for new standardized benchmarks that capture the
phenomena seen in robotics problems.

通过利用模拟器而无需进一步访问专家演示，我们证明了一类广泛存在的问题，这些问题可以在理论和实践上缓解移量漂移的影响，并详细说明了需要新的标准化基准来捕捉机器人问题中的现象。