Embodied agents maintain persistent identity while evolving modular capabilities through a closed-loop process, raising simulated task success from 32.4% to 91.3% with zero policy drift.
Learning without forgetting
2 Pith papers cite this work. Polarity classification is still indexing.
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A data-free class-incremental learning method for gesture recognition using prototype-guided pseudo feature replay with four components that achieves 11.8% and 12.8% mean global accuracy gains on SHREC 2017 3D and EgoGesture 3D datasets.
citing papers explorer
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Learning Without Losing Identity: Capability Evolution for Embodied Agents
Embodied agents maintain persistent identity while evolving modular capabilities through a closed-loop process, raising simulated task success from 32.4% to 91.3% with zero policy drift.
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Data-Free Class-Incremental Gesture Recognition with Prototype-Guided Pseudo Feature Replay
A data-free class-incremental learning method for gesture recognition using prototype-guided pseudo feature replay with four components that achieves 11.8% and 12.8% mean global accuracy gains on SHREC 2017 3D and EgoGesture 3D datasets.