SPIRE: 自律的な状況推論と因果的運動プリミティブ再利用によるタスク非依存の身体性継続学習
SPIRE: Task-agnostic embodied continual learning through autonomous situation inference and causal motor-primitive reuse
行動と結果の証拠から潜在的な状況を推論し、成熟した運動スキルを保護・再利用することで、タスクラベルなしに新しい制御課題の学習を加速するリカレントスパイキング型の継続学習システムを提案した。
著者: Yiyang Jia, Mingyang Lyu, Gongce Cao, Feifei Zhao, Yinqian Sun, Yi Zeng
分類: cs.LG, cs.RO
原文アブストラクト
Embodied continual learning requires controllers to infer changing situations, retain prior behavior and reuse motor skills without task labels or known boundaries. The challenge is deciding when to recover earlier behavior and which skills can support new learning. We introduce Situation-guided Primitive Integration for Recurrent Embodiment (SPIRE), a recurrent-spiking system that infers latent contexts from action-outcome evidence and learns context-specific motor actors online. Mature actors become protected sources that combine with a target-context residual to drive recurrent-spiking motor populations. Across coordinate, three-joint and hidden-force control, SPIRE maintained control, autonomously inferred situations, retained prior behavior and accelerated acquisition of an unseen source composition, reducing mean interactions to criterion by approximately 40% relative to matched fresh controllers. Rewinding compatible source actors impaired early acquisition in every matched intervention, demonstrating causal reuse. Against seven recurrent continual-learning and modular-control comparators, SPIRE achieved 2.4 times the mean prior-learning advantage of the strongest eligible task-free alternative selected for each paired stream. These results demonstrate autonomous recovery and causal reuse of motor skills for new learning in continuous embodied control.