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Title: Scene-level Programming by Demonstration
Scene-level Programming by Demonstration (PbD) is faced with an important challenge - perceptual uncertainty. Addressing this problem, we present a scene-level PbD paradigm that programs robots to perform goal-directed manipulation in unstructured environments with grounded perception. Scene estimation is enabled by our discriminatively-informed generative scene estimation method (DIGEST). Given scene observations, DIGEST utilizes candidates from discriminative object detectors to generate and evaluate hypothesized scenes of object poses. Scene graphs are generated from the estimated object poses, which in turn is used in the PbD system for high-level task planning. We demonstrate that DIGEST performs better than existing method and is robust to false positive detections. Building a PbD system on DIGEST, we show experiments of programming a Fetch robot to set up a tray for delivery with various objects through demonstration of goal scenes.
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