arXiv:2310.20414
Meta Learning for Multi-View Visuomotor Systems
원문 초록
This paper introduces a new approach for quickly adapting a multi-view visuomotor system for robots to varying camera configurations from the baseline setup. It utilises meta-learning to fine-tune the perceptual network while keeping the policy network fixed. Experimental results demonstrate a significant reduction in the number of new training episodes needed to attain baseline performance.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
DIP: Unsupervised Dense In-Context Post-training of Visual Representations
Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training
Meta-learning how to Share Credit among Macro-Actions
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.