From 2f16b3241661439b4d5585944db9ce67cdd312a3 Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Tue, 10 Dec 2024 07:00:31 +0000 Subject: [PATCH] Update 2023-12-02-tai23a.md --- _posts/2023-12-02-tai23a.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2023-12-02-tai23a.md b/_posts/2023-12-02-tai23a.md index f9fcfe9..90a56d3 100644 --- a/_posts/2023-12-02-tai23a.md +++ b/_posts/2023-12-02-tai23a.md @@ -16,7 +16,7 @@ abstract: "Effectively scooping food items poses a substantial challenge for cur SCONE is capable of capturing properties of food items and vital state characteristics. In our real-world scooping experiments, SCONE excels with a $71%$ success rate when tasked with 6 previously unseen food items across three different difficulty levels, - surpassing state-of-the\x02art methods. This enhanced performance underscores SCONE’s + surpassing state-of-the-art methods. This enhanced performance underscores SCONE’s stability, as all food items consistently achieve task success rates exceeding $50%$. Additionally, SCONE’s impressive capacity to accommodate diverse initial states enables it to precisely evaluate the present condition of the food, resulting in