Sacrifice as Paradigm of Robot Behavior in Group

2019 ◽  
pp. 244-254
Author(s):  
Viacheslav Abrosimov ◽  
Alexander Mochalkin
Keyword(s):  
Robot Behavior

Fuzzy Self-Adaptive Soccer Robot Behavior Decision System Design through ROS

2018 ◽  
Vol 62 (3) ◽  
pp. 304011-3040111 ◽  
Author(s):  
Shih-An Li ◽  
Hsuan-Ming Feng ◽  
Sheng-Po Huang ◽  
Chen-You Chu
Keyword(s):  
System Design ◽  
Soccer Robot ◽  
Decision System ◽  
Robot Behavior ◽  
Self Adaptive

scholarly journals Building the Foundation of Robot Explanation Generation Using Behavior Trees

2021 ◽  
Vol 10 (3) ◽  
pp. 1-31
Author(s):  
Zhao Han ◽  
Daniel Giger ◽  
Jordan Allspaw ◽  
Michael S. Lee ◽  
Henny Admoni ◽  
...  
Keyword(s):  
Autonomous Robots ◽  
Free Form ◽  
Static Structure ◽  
Simulation Code ◽  
Causal Information ◽  
Robot Behavior ◽  
Behavior Trees ◽  
Robot Task ◽  
Task Specification ◽  
Explanation Generation

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

scholarly journals Designing Environments Conducive to Interpretable Robot Behavior

2020 ◽  
Author(s):  
Anagha Kulkarni ◽  
Sarath Sreedharan ◽  
Sarah Keren ◽  
Tathagata Chakraborti ◽  
David E. Smith ◽  
...  
Keyword(s):  
Robot Behavior

Methods for Robot Behavior Adaptation for Cognitive Neurorehabilitation

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Alyssa Kubota ◽  
Laurel D. Riek
Keyword(s):  
State Of The Art ◽  
Cognitive Impairments ◽  
Autonomous Systems ◽  
Behavioral Adaptation ◽  
Annual Review ◽  
Publication Date ◽  
Pharmacological Treatments ◽  
Robot Behavior ◽  
Behavioral Treatments ◽  
Physical Abilities

An estimated 11% of adults report experiencing some form of cognitive decline, which may be associated with conditions such as stroke or dementia and can impact their memory, cognition, behavior, and physical abilities. While there are no known pharmacological treatments for many of these conditions, behavioral treatments such as cognitive training can prolong the independence of people with cognitive impairments. These treatments teach metacognitive strategies to compensate for memory difficulties in their everyday lives. Personalizing these treatments to suit the preferences and goals of an individual is critical to improving their engagement and sustainment, as well as maximizing the treatment's effectiveness. Robots have great potential to facilitate these training regimens and support people with cognitive impairments, their caregivers, and clinicians. This article examines how robots can adapt their behavior to be personalized to an individual in the context of cognitive neurorehabilitation. We provide an overview of existing robots being used to support neurorehabilitation and identify key principles for working in this space. We then examine state-of-the-art technical approaches for enabling longitudinal behavioral adaptation. To conclude, we discuss our recent work on enabling social robots to automatically adapt their behavior and explore open challenges for longitudinal behavior adaptation. This work will help guide the robotics community as it continues to provide more engaging, effective, and personalized interactions between people and robots. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations

2020 ◽  
Vol 34 (03) ◽  
pp. 2518-2526
Author(s):  
Sarath Sreedharan ◽  
Tathagata Chakraborti ◽  
Christian Muise ◽  
Subbarao Kambhampati
Keyword(s):  
Decision Making ◽  
Complete Solution ◽  
Robot Behavior ◽  
Computational Advantage ◽  
User Expectations ◽  
Expertise Level ◽  
Planning Problems

In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations (Chakraborti et al. 2017) and explicability (Kulkarni et al. 2019), but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to planning with diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.

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