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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SUM: Sequential Scene Understanding and Manipulation
In order to perform autonomous sequential manipulation tasks, perception in cluttered scenes remains a critical challenge for robots. In this paper, we propose a probabilistic approach for robust sequential scene estimation and manipulation - Sequential Scene Understanding and Manipulation(SUM). SUM considers uncertainty due to discriminative object detection and recognition in the generative estimation of the most likely object poses maintained over time to achieve a robust estimation of the scene under heavy occlusions and unstructured environment. Our method utilizes candidates from discriminative object detector and recognizer to guide the generative process of sampling scene hypothesis, and each scene hypotheses is evaluated against the observations. Also SUM maintains beliefs of scene hypothesis over robot physical actions for better estimation and against noisy detections. We conduct extensive experiments to show that our approach is able to perform robust estimation and manipulation.
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- Award ID(s):
- 1638047
- NSF-PAR ID:
- 10032699
- Date Published:
- Journal Name:
- arXiv.org
- ISSN:
- 2331-8422
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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