2 research outputs found
Enabling human-like task identification from natural conversation
A robot as a coworker or a cohabitant is becoming mainstream day-by-day with
the development of low-cost sophisticated hardware. However, an accompanying
software stack that can aid the usability of the robotic hardware remains the
bottleneck of the process, especially if the robot is not dedicated to a single
job. Programming a multi-purpose robot requires an on the fly mission
scheduling capability that involves task identification and plan generation.
The problem dimension increases if the robot accepts tasks from a human in
natural language. Though recent advances in NLP and planner development can
solve a variety of complex problems, their amalgamation for a dynamic robotic
task handler is used in a limited scope. Specifically, the problem of
formulating a planning problem from natural language instructions is not
studied in details. In this work, we provide a non-trivial method to combine an
NLP engine and a planner such that a robot can successfully identify tasks and
all the relevant parameters and generate an accurate plan for the task.
Additionally, some mechanism is required to resolve the ambiguity or missing
pieces of information in natural language instruction. Thus, we also develop a
dialogue strategy that aims to gather additional information with minimal
question-answer iterations and only when it is necessary. This work makes a
significant stride towards enabling a human-like task understanding capability
in a robot
DeComplex: Task planning from complex natural instructions by a collocating robot
As the number of robots in our daily surroundings like home, office,
restaurants, factory floors, etc. are increasing rapidly, the development of
natural human-robot interaction mechanism becomes more vital as it dictates the
usability and acceptability of the robots. One of the valued features of such a
cohabitant robot is that it performs tasks that are instructed in natural
language. However, it is not trivial to execute the human intended tasks as
natural language expressions can have large linguistic variations. Existing
works assume either single task instruction is given to the robot at a time or
there are multiple independent tasks in an instruction. However, complex task
instructions composed of multiple inter-dependent tasks are not handled
efficiently in the literature. There can be ordering dependency among the
tasks, i.e., the tasks have to be executed in a certain order or there can be
execution dependency, i.e., input parameter or execution of a task depends on
the outcome of another task. Understanding such dependencies in a complex
instruction is not trivial if an unconstrained natural language is allowed. In
this work, we propose a method to find the intended order of execution of
multiple inter-dependent tasks given in natural language instruction. Based on
our experiment, we show that our system is very accurate in generating a viable
execution plan from a complex instruction