Planning for serendipity

Chakraborti, Tathagata; Briggs, Gordon; Talamadupula, Kartik; Zhang, Yu; Scheutz, Matthias; Smith, David E.; Kambhampati, Subbarao

doi:10.1109/iros.2015.7354125

Cited by 27 publications

(26 citation statements)

References 8 publications

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“…Furthermore, to work alongside of humans, these agents must be "human-aware" when synthesizing plans. In prior works, this issue is addressed under human-aware planning [22,4,2] in which agents take the human's activities and intents into account when constructing their plans. This corresponds to human modeling in human-aware planning as shown in the second scenario in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…θ and β can then be computed given the mappings in Defs. (1) and (2). These measures can then be used to choose a plan that is more explicable and predictable.…”

Section: B Using the Learned Modelmentioning

confidence: 99%

See 2 more Smart Citations

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: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…Now, when acting in the presence of humans, the robot has not only to consider constraints such as pertinence and acceptation by the humans of its activity but also their course of actions. Most approaches to the so-called human-aware task planning problem assume a fully controllable and cooperative human, willing to participate in the accomplishment of a common goal [5,17,20]. Then, information and plan sharing are done in a post-processing step or at execution time [8,16].…”

Section: Introductionmentioning

confidence: 99%

A Human-Aware Task Planner Explicitly Reasoning About Human and Robot Decision, Action and Reaction

Buisan

Alami

2021

Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

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The complexity of the tasks autonomous robots can tackle is constantly increasing, yet we seldom see robots interacting with humans to perform tasks. Indeed, humans are either requested for punctual help or given the lead on the whole task. We propose a human-aware task planning approach allowing the robot to plan for a task while also considering and emulating the human decision, action, and reaction processes. Our approach is based on the exploration of multiple hierarchical tasks networks albeit differently whether the agent is considered to be controllable (the robot) or uncontrollable (the human(s)). We present the rationale of our approach along with a formalization and show its potential on an illustrative example involving the assembly of a table by a robot and a human.

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“…The challenge is how the robot can utilize this information to synthesize a plan while avoiding conflicts or providing proactive assistance [2,5]. There are different approaches to planning with such consideration [1,4]. Another the key consideration is to be socially acceptable [8,15], where the robot must be aware of expectation of the human teammates and acts accordingly.…”

Section: Introductionmentioning

confidence: 99%

Interactive Plan Explicability in Human-Robot Teaming

Zakershahrak

Sonawane

Gong

et al. 2018

2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

Self Cite

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Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status, but also be aware that its human teammates' expectation of itself. Being aware of the human teammates' expectation leads to robot behaviors that better align with human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot cooperation with the consideration of such teammate models in sequential domains by leveraging the concept of plan explicability. In plan explicability, however, the human is considered solely as an observer. In this paper, we extend plan explicability to consider interactive settings where human and robot behaviors can influence each other. We term this new measure as Interactive Plan Explicability. We compare the joint plan generated with the consideration of this measure using the fast forward planner (FF) with the plan created by FF without such consideration, as well as the plan created with actual human subjects. Results indicate that the explicability score of plans generated by our algorithm is comparable to the human plan, and better than the plan created by FF without considering the measure, implying that the plans created by our algorithms align better with expected joint plans of the human during execution. This can lead to more efficient collaboration in practice.

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Planning for serendipity

Cited by 27 publications

References 8 publications

Plan explicability and predictability for robot task planning

Plan explicability and predictability for robot task planning

A Human-Aware Task Planner Explicitly Reasoning About Human and Robot Decision, Action and Reaction

Interactive Plan Explicability in Human-Robot Teaming

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