Natural language interface for programming sensoryenabled scenarios for humanrobot interaction Previous research has shown that robotmediated therapy may be effective in improving different mental or physical conditions, but this effectiveness strongly depends on how well the therapy can be translated to robot training. The goal of this study is to assist the endusers such as occupational and rehabilitation therapists to create without help of technical professional therapyspecific and sensoryenabled scenarios for the robotic assistant for use in an unstructured environment. The Cognitive Dimension of Notations framework was applied to assess the usability of the programming interface and the Cyclomatic complexity method was used to evaluate the complexity of the created robot scenarios. Eleven therapists with a mean age of 39 years working in the care for persons with visualandintellectual disabilities participated. The results show good usability of the interface, as measured via the CDN framework and the cyclomatic complexity analysis showed an increased complexity of the created by the occupational and rehabilitation therapistxe2x80x99s scenarios. The participants did not request for very specifically defined behaviors for the robot, and therefore descriptions in natural text can be successfully used for robot programming.

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.