Large text-pretrained Transformers can effectively act as efficient in-context imitation learning machines for robotics, without the need for any additional training on robotics data.
FLOWRETRIEVAL improves few-shot imitation learning in robotics by retrieving motion-similar data from prior datasets using optical flow representations, leading to more efficient policy learning compared to methods relying solely on visual or semantic similarity.
FLOWRETRIEVAL은 이전 데이터에서 유사한 동작을 검색하여 소수 샷 모방 학습을 개선하는 방법으로, 광학 흐름을 활용하여 시각적 유사성에 의존하지 않고 작업 간에 전이 가능한 저수준 동작을 포착합니다.
本文提出了一種名為 FLOWRETRIEVAL 的新方法,透過利用光流表徵從先前數據集中檢索具有相似運動模式的數據,以改進機器人小樣本模仿學習。