Recovering from failure by asking for help Citation

Robots inevitably fail, often without the ability to recover autonomously. We demonstrate an approach for enabling a robot to recover from failures by communicating its need for specific help to a human partner using natural language. Our approach automatically detects failures, then generates targeted spoken-language requests for help such as “Please give me the white table leg that is on the black table.” Once the human partner has repaired the failure condition, the system resumes full autonomy. We present a novel inverse semantics algorithm for generating effective help requests. In contrast to forward semantic models that interpret natural language in terms of robot actions and perception, our inverse semantics algorithm generates requests by emulating the human’s ability to interpret a request using theGeneralizedGroundingGraph (G3) framework. To assess the effectiveness of our approach, we present a corpusbased online evaluation, as well as an end-to-end user study, demonstrating that our approach increases the effectiveness of human interventions compared to static requests for help. Ross A. Knepper and Stefanie Tellex have contributed equally to this paper. This is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”. B Ross A. Knepper rak@cs.cornell.edu 1 Department of Computer Science, Cornell University, Ithaca, USA 2 Computer Science Department, Brown University, Providence, USA 3 Department of Engineering, University of Cambridge, Cambridge, UK 4 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA