Humans communicating with other humans use a feedback loop that enables errors to be detected and fixed, increasing overall interaction success. We aim to enable robots to participate in this feedback loop so that they elicit additional information from the person when they are confused and use that information to resolve ambiguity and infer the person’s needs. This technology will enable robots to interact fluidly with untrained users who communicate with them using language and gestures. People from all walks of life can benefit from robotic help with physical tasks, ranging from assisting a disabled veteran in his home by fetching objects to a captain coordinating with a robotic assistant on a search-and-rescue mission.
Our latest paper defines a mathematical framework for an item-fetching domain that allows a robot to increase the speed and accuracy of its ability to interpret a person’s requests y reasoning about its own uncertainty as well as processing implicit information (implicatures). We formalize the item delivery domain as a Partially Observable Markov Decision Process (POMDP), and approximately solve this POMDP in real time. Our model improves speed and accuracy of fetching tasks by asking relevant clarifying questions only when necessary. To measure our model’s improvements, we conducted a real world user study with 16 participants. Our model is 2.17 seconds faster (25% faster) than state-of-the-art baseline, while being 2.1% more accurate.
You can see the system in action in this video: when the user is close to the robot, it is able to interpret the gesture and immediately selects the correct object without asking a question. However when the user is farther away, the pointing gesture is more ambiguous. The robot asks a targeted question. After the user answers the question, the robot selects the correct object. For more information, see our paper, which was accepted into ICRA 2017!