Your instruction may be crisp, but not clear to me! The number of robots deployed in our daily surroundings is everincreasing. Even in the industrial setup, the use of coworker robots is increasing rapidly. These cohabitant robots perform various tasks as instructed by colocated human beings. Thus, a natural interaction mechanism plays a big role in the usability and acceptability of the robot, especially by a nonexpert user. The recent development in natural language processing (NLP) has paved the way for chatbots to generate an automatic response for usersxe2x80x99 query. A robot can be equipped with such a dialogue system. However, the goal of humanrobot interaction is not focused on generating a response to queries, but it often involves performing some tasks in the physical world. Thus, a system is required that can detect user intended task from the natural instruction along with the set of pre and postconditions. In this work, we develop a dialogue engine for a robot that can classify and map a task instruction to the robotxe2x80x99s capability. If there is some ambiguity in the instructions or some required information is missing, which is often the case in natural conversation, it asks an appropriate question(s) to resolve it. The goal is to generate minimal and pinpointed queries for the user to resolve an ambiguity. We evaluate our system for a telepresence scenario where a remote user instructs the robot for various tasks. Our study based on 12 individuals shows that the proposed dialogue strategy can help a novice user to effectively interact with a robot, leading to satisfactory user experience.

Enabling HumanLike Task Identification From Natural Conversation A robot as a coworker or a cohabitant is becoming mainstream daybyday with the development of lowcost 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 multipurpose 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 nontrivial 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 questionanswer iterations and only when it is necessary. This work makes a significant stride towards enabling a humanlike task understanding capability in a robot.