Concurrent Plan Recognition and Execution for Human-Robot Teams

By Steven James Levine, Brian Charles Williams. 2014

102 citations recorded by the source corpus.

There is a strong demand for robots to work in environments, such as aircraft manufacturing, where they share tasks with humans and must quickly adapt to each other's needs. To do so, a robot must both infer the intent of humans, and must adapt accordingly. The literature to date has made great progress on these two tasks - recognition and adaptation - but largely as separate research activities. In this paper, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically. Key to our approach is a task representation that uses choice to represent alternative plans for both the human and robot, allowing a single set of algorithms to simultaneously achieve recognition and adaptation. To achieve such fluidity, a labeled propagation mechanism is used where decisions made by the human and robot during execution are propagated to relevant future open choices, as determined by causal link analysis, narrowing the possible options that the human would reasonably take (hence achieving intent recognition) as well as the possible actions the robot could consistently take (adaptation). This paper introduces Pike, an executive for human-robot teamwork that quickly adapts and infers intent based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either robot or human). We evaluate Pike's performance and demonstrate it on a household task in a human-robot team testbed.

Subfields: Artificial Intelligence, Social Psychology

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Citations in the corpus

Plan explicability and predictability for robot task planning (2015, background): “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].”
Deliberation for autonomous robots: A survey (2017, background): “RMPL[225] Titan [226] Burton [228] Kirk [224] Drake [52] Pike [140] PAM Probabilistic Timed hierarchical automata Temporal Plan Networks Probabilistic TPNs, TPNs with Uncertainty Exploration & rescue robots Autonomous Spacecraft Specific Time management Diagnostic and recovery Integrated knowledge r”
Automated Planning and Acting (2016, background): “TPNUs of Levine and Williams [380] add the notion of uncertainty for contingent decisions taken by the environment and other agents.”