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Metareasoning for Planning and Execution in Autonomous Systems
Metareasoning is the process by which an autonomous system optimizes, specifically monitors and controls, its own planning and execution processes in order to operate more effectively in its environment. As autonomous systems rapidly grow in sophistication and autonomy, the need for metareasoning has become critical for efficient and reliable operation in noisy, stochastic, unstructured domains for long periods of time. This is due to the uncertainty over the limitations of their reasoning capabilities and the range of their potential circumstances. However, despite considerable progress in metareasoning as a whole over the last thirty years, work on metareasoning for planning relies on several assumptions that diminish its accuracy and practical utility in autonomous systems that operate in the real world while work on metareasoning for execution has not seen much attention yet. This dissertation therefore proposes more effective metareasoning for planning while expanding the scope of metareasoning to execution to improve the efficiency of planning and the reliability of execution in autonomous systems.
In particular, we offer a two-pronged framework that introduces metareasoning for efficient planning and reliable execution in autonomous systems. We begin by proposing two forms of metareasoning for efficient planning: (1) a method that determines when to interrupt an anytime algorithm and act on the current solution by using online performance prediction and (2) a method that tunes the hyperparameters of the anytime algorithm at runtime by using deep reinforcement learning. We then propose two forms of metareasoning for reliable execution: (3) a method that recovers from exceptions that can be encountered during operation by using belief space planning and (4) a method that maintains and restores safety during operation by using probabilistic planning
Learning When to Quit: Meta-Reasoning for Motion Planning
Anytime motion planners are widely used in robotics. However, the
relationship between their solution quality and computation time is not well
understood, and thus, determining when to quit planning and start execution is
unclear. In this paper, we address the problem of deciding when to stop
deliberation under bounded computational capacity, so called meta-reasoning,
for anytime motion planning. We propose data-driven learning methods,
model-based and model-free meta-reasoning, that are applicable to different
environment distributions and agnostic to the choice of anytime motion
planners. As a part of the framework, we design a convolutional neural
network-based optimal solution predictor that predicts the optimal path length
from a given 2D workspace image. We empirically evaluate the performance of the
proposed methods in simulation in comparison with baselines.Comment: 8 pages, 5 figures, Submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS), 202
Learning a Meta-Controller for Dynamic Grasping
Grasping moving objects is a challenging task that combines multiple
submodules such as object pose predictor, arm motion planner, etc. Each
submodule operates under its own set of meta-parameters. For example, how far
the pose predictor should look into the future (i.e., look-ahead time) and the
maximum amount of time the motion planner can spend planning a motion (i.e.,
time budget). Many previous works assign fixed values to these parameters
either heuristically or through grid search; however, at different moments
within a single episode of dynamic grasping, the optimal values should vary
depending on the current scene. In this work, we learn a meta-controller
through reinforcement learning to control the look-ahead time and time budget
dynamically. Our extensive experiments show that the meta-controller improves
the grasping success rate (up to 12% in the most cluttered environment) and
reduces grasping time, compared to the strongest baseline. Our meta-controller
learns to reason about the reachable workspace and maintain the predicted pose
within the reachable region. In addition, it assigns a small but sufficient
time budget for the motion planner. Our method can handle different target
objects, trajectories, and obstacles. Despite being trained only with 3-6
randomly generated cuboidal obstacles, our meta-controller generalizes well to
7-9 obstacles and more realistic out-of-domain household setups with unseen
obstacle shapes. Video is available at https://youtu.be/CwHq77wFQqI.Comment: 10 page
A validated ontology for meta-level control domain
The main objective of meta-level control is to decide what and how much reasoning to do instead of what actions to do. Meta-level control domain involves a large number of processes and actions with terminology that become confusing. For this reason, an ontology to describe the semantic relationships and hierarchical structure of terms related to metacognition is proposed. The ontology was developed based on definitions found in the literature. Experts validated the ontology using a survey. The validation result indicated that the design of an ontology based on the meta-level control domain allows reusing and sharing knowledge defining a common vocabulary
Anytime Cognition: An information agent for emergency response
Planning under pressure in time-constrained environments while relying on uncertain information is a challenging task. This is particularly true for planning the response during an ongoing disaster in a urban area, be that a natural one, or a deliberate attack on the civilian population. As the various activities pertaining to the emergency response need to be coordinated in response to multiple reports from the disaster site, a user finds itself cognitively overloaded. To address this issue, we designed the Anytime Cognition (ANTICO) concept to assist human users working in time-constrained environments by maintaining a manageable level of cognitive workload over time. Based on the ANTICO concept, we develop an agent framework for proactively managing a user’s changing information requirements by integrating information management techniques with probabilistic plan recognition. In this paper, we describe a prototype emergency response application in the context of a subset of the attacks devised by the American Department of Homeland Security
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