Automated Planning for Peer-to-peer Teaming and its Evaluation in Remote Human-Robot Interaction

Narayanan, Vignesh; Mendoza, Nathaniel; Kambhampati, Subbarao

doi:10.1145/2701973.2702042

Cited by 8 publications

(7 citation statements)

References 3 publications

(4 reference statements)

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“…In the human-robot interaction (HRI) community, there exists prior works that discuss how to enable natural and fluent human-robot interaction [11,12,21,18] to create more socially acceptable robots [7]. These works, however, apply only to behaviors in specific domains.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, its interpretation from the human's perspective can be arbitrarily different from the agent's own model. While there exists previous work that studied socially acceptable robots [11,12,21,18] that interact with humans in "natural ways", and work that investigated legible motion planning [6], there lacks a general solution for high level task planning.…”

Section: Introductionmentioning

confidence: 99%

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Plan explicability and predictability for robot task planning

Sreedharan

Kulkarni

Chakraborti

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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101

107

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Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave unexpectedly. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in "natural ways", and work that investigated legible motion planning, there lacks a general solution for high level task planning. To address this issue, we introduce the notions of plan explicability and predictability. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with human subjects using physical robots to show the effectiveness of our approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plan explicability and predictability for robot task planning

Sreedharan

Kulkarni

Chakraborti

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

101

107

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“…Human-Robot Teaming / Cohabitation: Incorporation of the human model HuM in the planning process allows the robot to take into consideration possible human participation in the task and thus identify its appropriate role in it. This can relevant both when the robot is explicitly teaming [77], [52], [84], [78] with the human, or when it is just sharing or cohabiting [11], [17], [12], [16] the same workspace withouts shared goals and commitments. We have explored the typical roles of the robot in each of these scenariose.g.…”

Section: A Human Aware Planningmentioning

confidence: 99%

“…We have explored the typical roles of the robot in each of these scenariose.g. in planning for serendipity [11] and in planning with resource conflicts [17] we looked at how a robot can plan for passive coordination with minimal prior communication, while in [52], [84] we explored the effects of proactive support on the human teammate. Indeed, much of existing literature Fig.…”

Section: A Human Aware Planningmentioning

confidence: 99%

“…To this end, we have developed a suite of testbeds or microworlds for generation and testing of hypothesis and rapid prototyping of solutions. Figure 7 illustrate some of the microworlds we have used so far -anticlockwise from the left, this includes [1-2] a shared workspace [13] between humans and semi-autonomous agents that supports communication across various modalities such as speech, brainwaves (EEG) and augmented reality (AR) [video: https://goo.gl/KdsEgr]; [4][5] simulated Urban Search and Rescue scenarios [52], [84], [73], [74] with internal semi-autonomous agents (humans and robots) supervised by external human teammates [video: https: //goo.gl/BKHnSZ]; and [3,6] simulated (such as autonomous driving and collaborative assembly) domains for the study of multi-model planning [87], [43], [84], [52] with humans in the loop [video: https://goo.gl/UpEmzG]. The aim here is to conduct human-human or Wizard of Oz studies [7] in controlled settings and replicate desired behavior in the design of cognitive teammates.…”

Section: Evaluation / Microworldsmentioning

confidence: 99%

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AI Challenges in Human-Robot Cognitive Teaming

Chakraborti¹,

Kambhampati²,

Scheutz³

2017

Preprint

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Among the many anticipated roles for robots in the future is that of being a human teammate. Aside from all the technological hurdles that have to be overcome with respect to hardware and control to make robots fit to work with humans, the added complication here is that humans have many conscious and subconscious expectations of their teammates -indeed, we argue that teaming is mostly a cognitive rather than physical coordination activity. This introduces new challenges for the AI and robotics community and requires fundamental changes to the traditional approach to the design of autonomy. With this in mind, we propose an update to the classical view of the intelligent agent architecture, highlighting the requirements for mental modeling of the human in the deliberative process of the autonomous agent. In this article, we outline briefly the recent efforts of ours, and others in the community, towards developing cognitive teammates along these guidelines.

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Designing Interactions with Intention-Aware Gaze-Enabled Artificial Agents

Newn

Singh

Allison

et al. 2019

Human-Computer Interaction – INTERACT 2019

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As it becomes more common for humans to work alongside artificial agents on everyday tasks, it is increasingly important to design artificial agents that can understand and interact with their human counterparts naturally. We posit that an effective way to do this is to harness nonverbal cues used in humanhuman interaction. We, therefore, leverage knowledge from existing work on gazebased intention recognition, where the awareness of gaze can provide insights into the future actions of an observed human subject. In this paper, we design and evaluate the use of a proactive intention-aware gaze-enabled artificial agent that assists a human player engaged in an online strategy game. The agent assists by recognising and communicating the intentions of a human opponent in real-time, potentially improving situation awareness. Our first study identifies the language requirements for the artificial agent to communicate the opponent's intentions to the assisted player, using an inverted Wizard of Oz method approach. Our second study compares the experience of playing an online strategy game with and without the assistance of the agent. Specifically, we conducted a within-subjects study with 30 participants to compare their experience of playing with (1) detailed AI predictions, (2) abstract AI predictions, and (3) no AI predictions but with a live visualisation of their opponent's gaze. Our results show that the agent can facilitate awareness of another user's intentions without adding visual distraction to the interface; however, the cognitive workload was similar across all three conditions, suggesting that the manner in which the agent communicates its predictions requires further exploration. Overall, our work contributes to the understanding of how to support human-agent teams in a dynamic collaboration scenario. We provide a positive account of humans interacting with an intention-aware artificial agent afforded by gaze input, which presents immediate opportunities for improving interactions between humans and agents and demonstrates the usefulness of human elicitation for producing communication protocols for effective interaction.

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Automated Planning for Peer-to-peer Teaming and its Evaluation in Remote Human-Robot Interaction

Cited by 8 publications

References 3 publications

Plan explicability and predictability for robot task planning

Plan explicability and predictability for robot task planning

AI Challenges in Human-Robot Cognitive Teaming

Designing Interactions with Intention-Aware Gaze-Enabled Artificial Agents

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