Corpus ID: 218486850

"Can you do this?" Self-Assessment Dialogues with Autonomous Robots Before, During, and After a Mission

@article{Frasca2020CanYD,
  title={"Can you do this?" Self-Assessment Dialogues with Autonomous Robots Before, During, and After a Mission},
  author={Tyler M. Frasca and Evan A. Krause and Ravenna Thielstrom and matthias. scheutz},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.01544}
}
Autonomous robots with sophisticated capabilities can make it difficult for human instructors to assess its capabilities and proficiencies. Therefore, it is important future robots have the ability to: introspect on their capabilities and assess their task performance. Introspection allows the robot to determine what it can accomplish and self-assessment allows the robot estimate the likelihood it will accomplish at given task. We introduce a general framework for introspection and self… Expand
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References

SHOWING 1-8 OF 8 REFERENCES
How can a robot evaluate its own behavior? A neural model for self-assessment
TLDR
This work presents two different sensorimotor strategies the authors' robot can use to navigate and presents a neural architecture that may be able to evaluate both navigation strategies, based on an online novelty detection algorithm using a neural predictor. Expand
Analogical Generalization of Actions from Single Exemplars in a Robotic Architecture
TLDR
This paper shows how a robot can generate variations of a given scenario and then use the results of those new scenarios run in a physics simulator to generate generalized action scripts using analogical mappings. Expand
Crossing Boundaries: Multi-Level Introspection in a Complex Robotic Architecture for Automatic Performance Improvements
TLDR
This work introduces a novel multi-level introspection framework that can be used to automatically adjust architectural configurations based on the introspection results at the agent, infrastructure and component level and demonstrates the utility of such adjustments in a concrete implementation on a robot. Expand
DIARC: A Testbed for Natural Human-Robot Interaction
TLDR
The architecture and and its implementation in ADE is described, paying particular attention to its interaction capabilities and features that allow robust operation. Expand
Novel Mechanisms for Natural Human-Robot Interactions in the DIARC Architecture
Natural human-like human-robot interactions require many functional capabilities from a robot that have to be reflected in architectural components in the robotic control architecture. In particular,Expand
A game theoretic queueing approach to self-assessment in human-robot interaction systems
TLDR
A queueing model that addresses robot self-assessment in human-robot-interaction systems based on a game theoretic queueing approach is presented, and the impact of task heterogeneity in the optimal service decision-making and system performance is analyzed. Expand
Knowing your limits - self-evaluation and prediction in object recognition
TLDR
Probabilistic measures for observed detection success, predicted detection success and the completeness of learned models are presented, where learning is incremental and online. Expand
An Overview of the Distributed Integrated Affect and Reflection Cognitive DIARC Architecture
  • In Cognitive Architectures. Intelligent Systems, Control and Automation: Science and Engineering book series,
  • 2019