EVATAR – A Prototyping Middleware Embodying Virtual Agents to Autonomous Robots

Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.

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Investigating Trust in Interaction with Inconsistent Embodied Virtual Agents

International Journal of Social Robotics ◽

10.1007/s12369-021-00747-z ◽

2021 ◽

Author(s):

Reza Moradinezhad ◽

Erin T. Solovey

Keyword(s):

Virtual Agents

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Symbiosis with artificial intelligence via the prism of law, robots, and society

Artificial Intelligence and Law ◽

10.1007/s10506-021-09289-1 ◽

2021 ◽

Author(s):

Stamatis Karnouskos

Keyword(s):

Artificial Intelligence ◽

Social Economic ◽

Autonomous Robots ◽

Law And Society ◽

Citizenship Rights ◽

Legal Entity ◽

Profound Impact ◽

The People ◽

Physical Presence ◽

And Robotics

AbstractThe rapid advances in Artificial Intelligence and Robotics will have a profound impact on society as they will interfere with the people and their interactions. Intelligent autonomous robots, independent if they are humanoid/anthropomorphic or not, will have a physical presence, make autonomous decisions, and interact with all stakeholders in the society, in yet unforeseen manners. The symbiosis with such sophisticated robots may lead to a fundamental civilizational shift, with far-reaching effects as philosophical, legal, and societal questions on consciousness, citizenship, rights, and legal entity of robots are raised. The aim of this work is to understand the broad scope of potential issues pertaining to law and society through the investigation of the interplay of law, robots, and society via different angles such as law, social, economic, gender, and ethical perspectives. The results make it evident that in an era of symbiosis with intelligent autonomous robots, the law systems, as well as society, are not prepared for their prevalence. Therefore, it is now the time to start a multi-disciplinary stakeholder discussion and derive the necessary policies, frameworks, and roadmaps for the most eminent issues.

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The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

Social Science Computer Review ◽

10.1177/08944393211006863 ◽

2021 ◽

pp. 089443932110068

Author(s):

Aleksandra Urman ◽

Mykola Makhortykh ◽

Roberto Ulloa

Keyword(s):

Search Engine ◽

Search Engines ◽

Large Scale ◽

Web Search ◽

Primary Elections ◽

Virtual Agents ◽

Search Results ◽

Presidential Primary ◽

Large Scale Analysis ◽

Algorithmic Information

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

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Building the Foundation of Robot Explanation Generation Using Behavior Trees

ACM Transactions on Human-Robot Interaction ◽

10.1145/3457185 ◽

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.

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Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-020-01303-z ◽

2021 ◽

Vol 101 (3) ◽

Author(s):

Korbinian Nottensteiner ◽

Arne Sachtler ◽

Alin Albu-Schäffer

Keyword(s):

Autonomous Robots ◽

Vision System ◽

Dynamic Environments ◽

Bayesian Framework ◽

Experimental Results ◽

Robotic Assembly ◽

Tactile Sensing ◽

Task Execution ◽

Industrial Assembly ◽

Assembly Tasks

AbstractRobotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

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