Augmenting Knowledge through Statistical, Goaloriented HumanRobot Dialog Some robots can interact with humans using natural language, and identify service requests through humanrobot dialog. However, few robots are able to improve their language capabilities from this experience. In this paper, we develop a dialog agent for robots that is able to interpret user commands using a semantic parser, while asking clarification questions using a probabilistic dialog manager. This dialog agent is able to augment its knowledge base and improve its language capabilities by learning from dialog experiences, e.g., adding new entities and learning new ways of referring to existing entities. We have extensively evaluated our dialog system in simulation as well as with human participants through MTurk and realrobot platforms. We demonstrate that our dialog agent performs better in efficiency and accuracy in comparison to baseline learning agents. Demo video can be found at https:youtu.beDFB3jbHBqYE

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.