82,483 research outputs found
Towards Bridging the Gap between High-Level Reasoning and Execution on Robots
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.Comment: PhD Thesi
An LP-Based Approach for Goal Recognition as Planning
Goal recognition aims to recognize the set of candidate goals that are
compatible with the observed behavior of an agent. In this paper, we develop a
method based on the operator-counting framework that efficiently computes
solutions that satisfy the observations and uses the information generated to
solve goal recognition tasks. Our method reasons explicitly about both partial
and noisy observations: estimating uncertainty for the former, and satisfying
observations given the unreliability of the sensor for the latter. We evaluate
our approach empirically over a large data set, analyzing its components on how
each can impact the quality of the solutions. In general, our approach is
superior to previous methods in terms of agreement ratio, accuracy, and spread.
Finally, our approach paves the way for new research on combinatorial
optimization to solve goal recognition tasks.Comment: 8 pages, 4 tables, 3 figures. Published in AAAI 2021. Updated final
authorship and tex
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior
We aim to reduce the burden of programming and deploying autonomous systems
to work in concert with people in time-critical domains, such as military field
operations and disaster response. Deployment plans for these operations are
frequently negotiated on-the-fly by teams of human planners. A human operator
then translates the agreed upon plan into machine instructions for the robots.
We present an algorithm that reduces this translation burden by inferring the
final plan from a processed form of the human team's planning conversation. Our
approach combines probabilistic generative modeling with logical plan
validation used to compute a highly structured prior over possible plans. This
hybrid approach enables us to overcome the challenge of performing inference
over the large solution space with only a small amount of noisy data from the
team planning session. We validate the algorithm through human subject
experimentation and show we are able to infer a human team's final plan with
83% accuracy on average. We also describe a robot demonstration in which two
people plan and execute a first-response collaborative task with a PR2 robot.
To the best of our knowledge, this is the first work that integrates a logical
planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on
Artificial Intelligence (AAAI-13
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