Soft growing robots map unknown 2D environments by characterizing collision deformations, building a geometry-based simulator, and using Monte Carlo sampling to select optimal deployments that approach ideal actions.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
APPLE is an RL framework that jointly optimizes a transformer perception module and policy via a unified objective for general active perception, with evaluations on tactile MNIST regression and classification tasks.
citing papers explorer
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Linking Exteroception and Proprioception through Improved Contact Modeling for Soft Growing Robots
Soft growing robots map unknown 2D environments by characterizing collision deformations, building a geometry-based simulator, and using Monte Carlo sampling to select optimal deployments that approach ideal actions.
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Apple: Toward General Active Perception via Reinforcement Learning
APPLE is an RL framework that jointly optimizes a transformer perception module and policy via a unified objective for general active perception, with evaluations on tactile MNIST regression and classification tasks.