Robots inevitably fail, often without the ability to recover autonomously. We demonstrate an approach for enabling a KUKA youBot 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 the Generalized Grounding Graph (G3 ) framework. To assess the effectiveness of our approach, we present a corpus-based 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. Accompanying paper by Stefanie Tellex, Ross A. Knepper, Adrian Li, Daniela Rus, and Nicholas Roy. This work was supported in part by the Boeing Company, and in part by the U.S Army Research Laboratory under the Robotics Collaborative Technology Alliance. The authors thank Dishaan Ahuja and Andrew Spielberg for their assistance in conducting the experiments.