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Creators/Authors contains: "Knepper, Ross A."

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  1. ; ( , International Symposium on Robotics Research (ISRR))
    We present a novel method for performing integrated task and motion planning (TMP) by adapting any off-the-shelf sampling-based motion planning algorithm to simultaneously solve for a symbolically and geometrically feasible plan using a single motion planner invocation. The core insight of our technique is an embedding of symbolic state into continuous space, coupled with a novel means of automatically deriving a function guiding a planner to regions of continuous space where symbolic actions can be executed. Our technique makes few assumptions and offers a great degree of flexibility and generality compared to state of the art planners. We describe our technique and offer a proof of probabilistic completeness along with empirical evaluation of our technique on manipulation benchmark problems. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ( , Proceedings of the Conference on Robot Learning (CoRL))
    Accepted Manuscript Full Text Available
  3. ; ; ; ( , Proceedings of The 2nd Conference on Robot Learning)
    We propose an approach for mapping natural language instructions and raw observations to continuous control of a quadcopter drone. Our model predicts interpretable position-visitation distributions indicating where the agent should go during execution and where it should stop, and uses the predicted distributions to select the actions to execute. This two-step model decomposition allows for simple and efficient training using a combination of supervised learning and imitation learning. We evaluate our approach with a realistic drone simulator, and demonstrate absolute task-completion accuracy improvements of 16.85% over two state-of-the-art instruction-following methods. 
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    Accepted Manuscript Full Text Available