Online decision making under uncertainty in partially observable domains,
also known as Belief Space Planning, is a fundamental problem in robotics and
Artificial Intelligence. Due to an abundance of plausible future unravelings,
calculating an optimal course of action inflicts an enormous computational
burden on the agent. Moreover, in many scenarios, e.g., information gathering,
it is required to introduce a belief-dependent constraint. Prompted by this
demand, in this paper, we consider a recently introduced probabilistic
belief-dependent constrained POMDP. We present a technique to adaptively accept
or discard a candidate action sequence with respect to a probabilistic
belief-dependent constraint, before expanding a complete set of future
observations samples and without any loss in accuracy. Moreover, using our
proposed framework, we contribute an adaptive method to find a maximal feasible
return (e.g., information gain) in terms of Value at Risk for the candidate
action sequence with substantial acceleration. On top of that, we introduce an
adaptive simplification technique for a probabilistically constrained setting.
Such an approach provably returns an identical-quality solution while
dramatically accelerating online decision making. Our universal framework
applies to any belief-dependent constrained continuous POMDP with parametric
beliefs, as well as nonparametric beliefs represented by particles. In the
context of an information-theoretic constraint, our presented framework
stochastically quantifies if a cumulative information gain along the planning
horizon is sufficiently significant (e.g. for, information gathering, active
SLAM). We apply our method to active SLAM, a highly challenging problem of high
dimensional Belief Space Planning. Extensive realistic simulations corroborate
the superiority of our proposed ideas.

研究了基于信念空间规划的在线决策问题，在信息收集等场景下，介绍了一种自适应的方法来寻求最大可行回报，应用这种方法可以在保证准确率的前提下显著加速在线决策过程，并进行了大量现实模拟来验证此方法的优越性。

自适应概率信赖约束下的连续高维信念空间规划

Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints

Autonomous agents operating in perceptually aliased environments should
ideally be able to solve the data association problem. Yet, planning for future
actions while considering this problem is not trivial. State of the art
approaches therefore use multi-modal hypotheses to represent the states of the
agent and of the environment. However, explicitly considering all possible data
associations, the number of hypotheses grows exponentially with the planning
horizon. As such, the corresponding Belief Space Planning problem quickly
becomes unsolvable. Moreover, under hard computational budget constraints, some
non-negligible hypotheses must eventually be pruned in both planning and
inference. Nevertheless, the two processes are generally treated separately and
the effect of budget constraints in one process over the other was barely
studied. We present a computationally efficient method to solve the nonmyopic
Belief Space Planning problem while reasoning about data association. Moreover,
we rigorously analyze the effects of budget constraints in both inference and
planning.

本文提出了一种计算效率高的算法来解决智能体在感知受扰环境中的非近视信念空间规划问题，同时分析了预算限制对推理和规划的影响。

预算约束下的非远见式蒸馏数据关联置信度空间规划

Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints

Unresolved data association in ambiguous and perceptually aliased
environments leads to multi-modal hypotheses on both the robot's and the
environment state. To avoid catastrophic results, when operating in such
ambiguous environments, it is crucial to reason about data association within
Belief Space Planning (BSP). However, explicitly considering all possible data
associations, the number of hypotheses grows exponentially with the planning
horizon and determining the optimal action sequence quickly becomes
intractable. Moreover, with hard budget constraints where some non-negligible
hypotheses must be pruned, achieving performance guarantees is crucial. In this
work we present a computationally efficient novel approach that utilizes only a
distilled subset of hypotheses to solve BSP problems while reasoning about data
association. Furthermore, to provide performance guarantees, we derive error
bounds with respect to the optimal solution. We then demonstrate our approach
in an extremely aliased environment, where we manage to significantly reduce
computation time without compromising on the quality of the solution.

在存在多模态假设和环境感知混淆情况下，本文提出了一种基于置信空间规划的高效数据关联计算方法，并证明了提出方法的正确性与可行性