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Register a new userRobotic Supervised Autonomy: A Review
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Yangming Li
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Authors
Yangming Li
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This invited paper discusses a new but important problem, supervised autonomy, in the context of robotics. The paper defines supervised autonomy and compares the supervised autonomy with robotic teleoperation and robotic full autonomy. Based on the discussion, the significance of supervised autonomy was introduced. The paper discusses the challenging and unsolved problems in supervised autonomy, and reviews the related works in our research lab. Based on the discussions, the paper draws the conclusion that supervised autonomy is critical for applying robotic systems to address complicated problems in the real world.
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This work tackles scene understanding for outdoor robotic navigation, solely relying on images captured by an on-board camera. Conventional visual scene understanding interprets the environment based on specific descriptive categories. However, such a representation is not directly interpretable for decision-making and constrains robot operation to a specific domain. Thus, we propose to segment egocentric images directly in terms of how a robot can navigate in them, and tailor the learning problem to an autonomous navigation task. Building around an image segmentation network, we present a generic and scalable affordance-based definition consisting of 3 driveability levels which can be applied to arbitrary scenes. By encoding these levels with soft ordinal labels, we incorporate inter-class distances during learning which improves segmentation compared to standard one-hot labelling. In addition, we propose a navigation-oriented pixel-wise loss weighting method which assigns higher importance to safety-critical areas. We evaluate our approach on large-scale public image segmentation datasets spanning off-road and urban scenes. In a zero-shot cross-dataset generalization experiment, we show that our affordance learning scheme can be applied across a diverse mix of datasets and improves driveability estimation in unseen environments compared to general-purpose, single-dataset segmentation.
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