Coordination in human-robot teams using mental modeling and plan recognition

Talamadupula, Kartik; Briggs, Gordon; Chakraborti, Tathagata; Scheutz, Matthias; Kambhampati, Subbarao

doi:10.1109/iros.2014.6942970

Cited by 40 publications

(39 citation statements)

References 10 publications

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“…[70] Distributed LTL specifications over multiple agents, exchanged when the robots physically meet and model checked for mutual satisfiability. N/A [164] First-Order Logic (FOL) to formalise agent beliefs and intentions, and uses these to predict the plan of other agents for cooperation.…”

Section: Ref Formalisms/tools Employedmentioning

confidence: 99%

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Formal Specification and Verification of Autonomous Robotic Systems

et al. 2019

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Autonomous robotic systems are complex, hybrid, and often safety-critical; this makes their formal specification and verification uniquely challenging. Though commonly used, testing and simulation alone are insufficient to ensure the correctness of, or provide sufficient evidence for the certification of, autonomous robotics. Formal methods for autonomous robotics has received some attention in the literature, but no resource provides a current overview. This paper systematically surveys the state-of-the-art in formal specification and verification for autonomous robotics. Specially, it identifies and categorises the challenges posed by, the formalisms aimed at, and the formal approaches for the specification and verification of autonomous robotics. Introduction, Methodology and Related WorkAn autonomous system is an artificially intelligent entity that makes decisions in response to input, independent of human interaction. Robotic systems are physical entities that interact with the physical world. Thus, we consider an autonomous robotic system as a machine that uses Artificial Intelligence (AI), has a physical presence in and interacts with the real world. They are complex, inherently hybrid, systems, combining both hardware and software; they often require close safety, legal, and ethical consideration. Autonomous robotics are increasingly being used in commonplace-scenarios, such as driverless cars [68], pilotless aircraft [176], and domestic assistants [174,60].While for many engineered systems, testing, either through real deployment or via simulation, is deemed sufficient; the unique challenges of autonomous robotics, their dependence on sophisticated software control and decision-making, and their increasing deployment in safety-critical scenarios, require a stronger form of verification. This leads us towards using formal methods, which are mathematically-based techniques for the specification and verification of software systems, to ensure the correctness of, and provide sufficient evidence for the certification of, robotic systems.We contribute an overview and analysis of the state-of-the-art in formal specification and verification of autonomous robotics. §1.1 outlines the scope, research questions and search criteria for our survey. §1.2 describes related work concerning formal methods for robotics and differentiates them from our work. We recognise the important role that middleware architectures and, non-and semi-formal techniques have in the development of reliable robotics and we briefly summarise some of these techniques in §2. The specification and verification challenges raised by autonomous robotic systems are discussed next: §3 describes the challenges of their context (the external challenges) and §4 describes the challenges of their organisation (the internal challenges). §5 discusses the formalisms used in the literature for specification and verification of autonomous robotics. §6 characterises the approaches to formal specification and verification of autonomous robotics found in the li...

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Section: Ref Formalisms/tools Employedmentioning

confidence: 99%

“…Talamadupula et al [164] use FOL to capture an agent's beliefs and intentions. They use this to enable the prediction of other agents' plans.…”

Section: Other Logicsmentioning

confidence: 99%

Formal Specification and Verification of Autonomous Robotic Systems

et al. 2019

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“…This information is then used to avoid interference, and plan for serendipity and teaming with humans. There exists a rich literature on plan recognition [14,3,20,16], and many recent works use these techniques in human-aware planning and human-robot teaming [23,2,25].…”

Section: Related Workmentioning

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)

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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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“…Further, the robot can also maintain belief models Bel α about the other agents in its environment as described in more detail in [2]. For the purposes of this paper, we assume that agents have complete information about the environment, and the robot has the complete domain as well as belief model of the humans.…”

Section: A Agent Modelsmentioning

confidence: 99%

“…Instead, contingent on the availability of the right information, the planner can anticipate, recognize, and further predict the future plans of other agents. Recent work [2] has seen the successful deployment of this idea in scenarios where a robotic agent is trying to coordinate its plan with that of a human, and where the agents are competing for the same resource(s) and must have their plans de-conflicted in some principled manner. Indeed there has been a lot of work under the umbrella of "human-aware" planning, both in the context of path planning [3], [4] and in task planning [5], [6], that aim to provide social skills to robots so as to make them produce plans conforming to desired behaviors when humans and robots operate in shared settings.…”

Section: Introductionmentioning

confidence: 99%

Planning for serendipity

Chakraborti

Briggs

Talamadupula

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Recently there has been a lot of focus on human robot co-habitation issues that are often orthogonal to many aspects of human-robot teaming; e.g. on producing socially acceptable behaviors of robots and de-conflicting plans of robots and humans in shared environments. However, an interesting offshoot of these settings that has largely been overlooked is the problem of planning for serendipity -i.e. planning for stigmergic collaboration without explicit commitments on agents in co-habitation. In this paper we formalize this notion of planning for serendipity for the first time, and provide an Integer Programming based solution for this problem. Further, we illustrate the different modes of this planning technique on a typical Urban Search and Rescue scenario and show a reallife implementation of the ideas on the Nao Robot interacting with a human colleague.

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Coordination in human-robot teams using mental modeling and plan recognition

Cited by 40 publications

References 10 publications

Formal Specification and Verification of Autonomous Robotic Systems

Formal Specification and Verification of Autonomous Robotic Systems

Plan explicability and predictability for robot task planning

Planning for serendipity

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