SUM: Sequential Scene Understanding and Manipulation
This addresses a critical challenge in robotics for performing tasks in unstructured settings, though it appears incremental as it builds on existing probabilistic methods.
The paper tackles the problem of robust scene estimation for autonomous sequential manipulation in cluttered, occluded environments by proposing a probabilistic approach that integrates discriminative object detection with generative scene hypothesis sampling, achieving improved estimation and manipulation performance.
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.