Preference-Based Assistance Prediction for Human-Robot Collaboration Tasks

Grigore, Elena Corina; Roncone, Alessandro; Mangin, Olivier; Scassellati, Brian

doi:10.1109/iros.2018.8593716

Cited by 25 publications

(17 citation statements)

References 21 publications

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“…We extrapolated the root terms and categorized them (e.g., explanation→explainability, expressive→expressivity). Assuming that papers with the same authors share the same definition, we grouped them by author, and [64,65] Predictability [32,67] Communicability [36,48] None [12,39,45,49,52] only report the most recent definition later in Table 6. When the definition was from an outside source not cited before, we mentioned both the definition and the reference.…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, Schaefer et al [64] investigated the understandability of the embodied agent's intentions for effective collaborations with humans. Following this idea, Grigore et al [32] referred to predictability, building upon hidden state representation.…”

Section: Methodsmentioning

confidence: 99%

“…We have claimed earlier that embodied agents can become explainable using unique types of social cues that are not available to agents lacking such embodiment. One example is the ability to point [16,32,39,48] to important objects in a scene-assuming the agent has an extremity that affords pointing. A non-embodied agent has to use a different way to communicate the importance of that object.…”

Section: Social Cuesmentioning

confidence: 99%

See 2 more Smart Citations

Explainable Embodied Agents Through Social Cues

Wallkotter

Tulli

Castellano

et al. 2021

J. Hum.-Robot Interact.

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The issue of how to make embodied agents explainable has experienced a surge of interest over the past 3 years, and there are many terms that refer to this concept, such as transparency and legibility. One reason for this high variance in terminology is the unique array of social cues that embodied agents can access in contrast to that accessed by non-embodied agents. Another reason is that different authors use these terms in different ways. Hence, we review the existing literature on explainability and organize it by (1) providing an overview of existing definitions, (2) showing how explainability is implemented and how it exploits different social cues, and (3) showing how the impact of explainability is measured. Additionally, we present a list of open questions and challenges that highlight areas that require further investigation by the community. This provides the interested reader with an overview of the current state of the art.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Social Cuesmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Embodied Agents Through Social Cues

Wallkotter

Tulli

Castellano

et al. 2021

J. Hum.-Robot Interact.

View full text Add to dashboard Cite

show abstract

“… Hayes and Scassellati (2015) presents an algorithm to generate supportive behaviors during collaborative activity, although its results in simulation fall short in terms of providing practical demonstrations of the technique. Grigore et al (2018) proposes a model to predict supportive behaviors from observed demonstration trajectories and hidden human preferences, but the results are not operationalized and evaluated within a full HRC system. On the other side of the spectrum, a number of works cited above achieve to a certain amount supportive behaviors without explicitly targeting them ( Hoffman and Breazeal, 2007 ; Shah et al, 2011 ; Gopalan and Tellex, 2015 ; Toussaint et al, 2016 ).…”

Section: Background and Related Workmentioning

confidence: 99%

How to be Helpful? Supportive Behaviors and Personalization for Human-Robot Collaboration

2022

Self Cite

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The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in recent years. Thanks in part to advances in control and perception algorithms, robots have started to work in increasingly unstructured environments, where they operate side by side with humans to achieve shared tasks. However, little progress has been made toward the development of systems that are truly effective in supporting the human, proactive in their collaboration, and that can autonomously take care of part of the task. In this work, we present a collaborative system capable of assisting a human worker despite limited manipulation capabilities, incomplete model of the task, and partial observability of the environment. Our framework leverages information from a high-level, hierarchical model that is shared between the human and robot and that enables transparent synchronization between the peers and mutual understanding of each other’s plan. More precisely, we firstly derive a partially observable Markov model from the high-level task representation; we then use an online Monte-Carlo solver to compute a short-horizon robot-executable plan. The resulting policy is capable of interactive replanning on-the-fly, dynamic error recovery, and identification of hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a realistic furniture construction task.

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“…Mimicking real-world HRI behaviors and simulating scenarios based on observable information are key features during human-robot collaboration (HRC) [68]. A predictive and adaptive system based on learning by demonstration is explained in [69].…”

Section: B Modern Developmentsmentioning

confidence: 99%

Proactive Robots With the Perception of Nonverbal Human Behavior: A Review

2019

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Intelligent robot companions contribute significantly in improving living standards in the modern society. Therefore, human-like decision making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its non-expert human user. On the other hand, the robot will have the capability of dealing with humans without causing any disturbance by the robot's behavior. Mimicing human emotional intelligence is one of the best and reasonable ways of laying the foundation for robotic emotional intelligence. As robots are widely deployed in social environments, perception of the situation or intentions of a user prior to an interaction is required to be proactive. Proactive robots are required to understand what is communicated by the human body language prior to approaching a human. Social constraints in an interaction could be demolished by this assessment in this regard. In this review, we incorporate various findings of human-robot interaction, social robotics and psychophysiology to assess intelligent systems which were capable of evaluating the emotional state of humans prior to an interaction. Second, we identify the cues and evaluation techniques that were utilized by such intelligent agents to simulate and evaluate the suitability of a proactive interaction. Available literature has been evaluated to distinguish limitations of existing methods and suggest possible improvements. These limitations, guiding principles to be adhered to and suggested improvements, are presented as an outcome of the review. INDEX TERMSInteraction initiation, nonverbal cues, context-awareness, social robots, human-robot interaction. A. G. BUDDHIKA P. JAYASEKARA (M'07-SM'17) received the B.Sc. and M.Sc. degrees in electrical engineering from the University of Moratuwa, respectively, in 2004 and 2006, and the doctoral degree in robotics and intelligent systems from Saga University, Japan. He received the Japanese Government Monbukagakusho Scholarship to pursue postgraduate studies in Japan. He joined the Department of Electrical Engineering, University of Moratuwa, after completing his doctoral studies in 2010, where he currently a Senior Lecturer.His main research interests include artificial intelligence techniques, human-robot interaction, human-friendly robotics, machine learning, and intelligent systems. He is the Principle Investigator of the Intelligent Service Robotics Group, Department of Electrical Engineering, University of Moratuwa, and working as the Director of Center for Advanced Robotics (CAR), a multidisciplinary research center of University of Moratuwa from 2017. Currently, he is working on two main projects: intelligent mobile robot for domestic environments (MIRob-Moratuwa Intelligent Robot) and intelligent wheelchair for domestic environments. D. P. CHANDIMA (M'03-SM'17) received the B.Sc. degree from the

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Preference-Based Assistance Prediction for Human-Robot Collaboration Tasks

Cited by 25 publications

References 21 publications

Explainable Embodied Agents Through Social Cues

Explainable Embodied Agents Through Social Cues

How to be Helpful? Supportive Behaviors and Personalization for Human-Robot Collaboration

Proactive Robots With the Perception of Nonverbal Human Behavior: A Review

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