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Revisiting Active Perception
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Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. We argue that those contributions are as relevant today as they were decades ago and, with the state of modern computational tools, are poised to find new life in the robotic perception systems of the next decade.
Forward citations
Cited by 3 Pith papers
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Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve...
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Vision in Action: Learning Active Perception from Human Demonstrations
ViA trains bimanual manipulation policies from human demonstrations that include active head-camera movement, using a 6-DoF robot neck and a VR interface with point-cloud rendering, reporting large gains on three occl...
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Gaussian Process-Based Active Exploration Strategies in Vision and Touch
A robot arm uses Gaussian Process Distance Fields to fuse RGBD vision and tactile contacts, actively choosing next views and touch points to reduce shape uncertainty, while material classification remains near chance.
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