Acting from Belief, Looking When Needed: A Bayesian Spatial
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Acting from Belief, Looking When Needed: A Bayesian Spatial World Model for Navigation under Intermittent Perception. ALONE, a Bayesian spatial world model, enables robot navigation under intermittent perception by using an internal spatial belief and selectively acquiring observations. It achieves 98% and 97% closed-loop success in sim…
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
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Environment: simulation
“mera depth images. Across two simulated scene families, it achieves 98% and 97% closed-loop success at a 10 Hz decision rate. Among successful trials, median fractions of decision steps requiring a new depth observation are only 0.9% and 1.3%, respectively, demonstrating high navigation success with substantially reduced observation demand. Real-world indoor flight experiments further validate navigation under interm”
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Reported quantity · successful trials: 10 other · reported trials
“ittent depth observations, with all 10 trials successful.”
Exact source · revision 1Source owner not reported
Reported quantity · decision rate: 10 other · Basis not reported
“mera depth images. Across two simulated scene families, it achieves 98% and 97% closed-loop success at a 10 Hz decision rate. Among successful trials, median fractions of decision steps requiring a new depth observation are only 0.9% and 1.3%, respectively, demonstrating high navigation success with substantially reduced observation demand. Real-world indoor flight experiments further validate navigation under interm”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsActing from Belief, Looking When Needed: A Bayesian Spatial World Model for Navigation under Intermittent Perception
ALONE, a Bayesian spatial world model, enables robot navigation under intermittent perception by using an internal spatial belief and selectively acquiring observations. It achieves 98% and 97% closed-loop success in simulation and validates real-world performance with 10 successful indoor flight trials.
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