既製基盤モデルによる学習不要なロボット姿勢推定
Training-Free Robot Pose Estimation using Off-the-Shelf Foundational Models
最先端の視覚言語モデルをそのまま使い、単一画像からロボットアームの関節角度を推定する手法を検証し、その性能限界を明らかにした。
著者: Laurence Liang
分類: cs.RO, eess.IV
原文アブストラクト
Pose estimation of a robot arm from visual inputs is a challenging task. However, with the increasing adoption of robot arms for both industrial and residential use cases, reliable joint angle estimation can offer improved safety and performance guarantees, and also be used as a verifier to further train robot policies. This paper introduces using frontier vision-language models (VLMs) as an ``off-the-shelf" tool to estimate a robot arm's joint angles from a single target image. By evaluating frontier VLMs on both synthetic and real-world image-data pairs, this paper establishes a performance baseline attained by current FLMs. In addition, this paper presents empirical results suggesting that test time scaling or parameter scaling alone does not lead to improved joint angle predictions.