Reproduce “Rendering-Free Lookahead for Question-Guided Active Vision” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Koya Sakamoto, Daichi Azuma, Shuhei Kurita, Naoya Chiba, Yusuke Iwasawa, Yutaka Matsuo, Taiki Miyanishi
RFL teaches a robot to select camera motions likely to reveal evidence for answering a question. During training, a teacher scores rendered future views with a frozen vision-language model; at deployment, a student ranks candidate moves without rendering future images. On 377 episodes in 21 unseen scenes, RFL scores 3.05 versus 2.14 for direct control; this benchmark does not establish real-robot transfer.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.