Human-robot shared workspace collaboration via hindsight optimization

Pellegrinelli, Stefania; Admoni, Henny; Javdani, Shervin; Srinivasa, Siddhartha S.

doi:10.1109/iros.2016.7759147

Cited by 41 publications

(18 citation statements)

References 25 publications

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“…Here, it is assumed that the collaborative agent has access to some underlying human reward function (usually inferred through IRL or inverse planning approaches). The human is modeled to act rationally with the highest probability, but with a non-zero probability of behaving sub-optimally [20,[47][48][49][50].…”

Section: First-order Mental Modelsmentioning

confidence: 99%

A Survey of Mental Modeling Techniques in Human–Robot Teaming

2020

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Purpose of Review As robots become increasingly prevalent and capable, the complexity of roles and responsibilities assigned to them as well as our expectations for them will increase in kind. For these autonomous systems to operate safely and efficiently in human-populated environments, they will need to cooperate and coordinate with human teammates. Mental models provide a formal mechanism for achieving fluent and effective teamwork during human-robot interaction by enabling awareness between teammates and allowing for coordinated action. Recent Findings Much recent research in human-robot interaction has made use of standardized and formalized mental modeling techniques to great effect, allowing for a wider breadth of scenarios in which a robotic agent can act as an effective and trustworthy teammate. Summary This paper provides a structured overview of mental model theory and methodology as applied to human-robot teaming. Also discussed are evaluation methods and metrics for various aspects of mental modeling during human-robot interaction, as well as recent emerging applications and open challenges in the field. Keywords Human-robot teaming • Mental models • Human-robot interaction • Theory of mind This article belongs to the Topical Collection on Service and Interactive Robotics

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Section: First-order Mental Modelsmentioning

confidence: 99%

A Survey of Mental Modeling Techniques in Human–Robot Teaming

2020

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“… 8. This study has been previously published in Pellegrinelli et al (2016). We include results and elaborate here for a unified presentation. …”

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confidence: 99%

Shared autonomy via hindsight optimization for teleoperation and teaming

Javdani

Admoni

Pellegrinelli

et al. 2018

The International Journal of Robotics Research

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165

152

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In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user's goal. As such, most prior works follow a predict-then-act model, first predicting the user's goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user's goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modelling shared autonomy as a Partially Observable Markov Decision Process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human-robot teaming. Compared to predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user-robot collisions.Prepared using sagej.cls [Version: 2015/06/09 v1.01] arXiv:1706.00155v1 [cs.RO] 1 Jun 2017 * While we assume the goal is fixed, we do not assume how the user will achieve that goal (e.g. grasp location) is fixed.

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“…An important aspect affecting human propensity to work with a robot companion is the ability of the robot to dynamically change and adapt its operation in line with human behaviour and actions. According to the state-of-the-art, robot actions can be optimised on the basis of human behaviour [75,180,194]. A step forward in this direction is represented by a set of studies [81,181], showing that the success of a human-robot team also depends on the human level of adaptability in different situations.…”

Section: Psychological Challenges In Human-robot Collaborationmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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Human-robot shared workspace collaboration via hindsight optimization

Cited by 41 publications

References 25 publications

A Survey of Mental Modeling Techniques in Human–Robot Teaming

A Survey of Mental Modeling Techniques in Human–Robot Teaming

Shared autonomy via hindsight optimization for teleoperation and teaming

Symbiotic human-robot collaborative assembly

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