AnyviewMeter: カメラ幾何とマルチビュー注意によるロボット報酬モデルの適応
AnyviewMeter: Adapting Robotic Reward Models with Camera Geometry and Multi-View Attention
カメラ視点の変化に頑健なロボット報酬モデルを実現するため、低ランク微調整とPlucker光線条件付け、同期ブロック注意を組み合わせた適応フレームワークを提案し、視点変更時の誤差を約21%削減した。
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著者: Yuang Tu, Runjia Tan, Yujie Yan, Jinghan Hu, Chen Lv
分類: cs.CV
原文アブストラクト
Robotic reward models evaluate task execution from visual observations, but their predictions can change with camera viewpoint and occlusion even when the underlying task state is unchanged. Adapting a pretrained reward model to a local task therefore requires accounting for how that task is observed. We introduce AnyviewMeter, a geometry-conditioned adaptation framework for robotic reward models that represent task progress as a scalar reward signal. It combines low-rank fine-tuning with token-aligned Plucker rays and synchronous block attention: ray conditioning incorporates camera geometry into visual features and attention queries and keys, while block attention fuses synchronized views inside the pretrained decoder. The framework supports both single-view reward prediction and joint multi-view evaluation through parameter-efficient adaptation of a pretrained Robometer model. On PickCube, single-view adaptation improves progress prediction in every camera group and reduces mean absolute error under a changed field of view by approximately 21% relative to RGB fine-tuning. Across simulated manipulation tasks, joint multi-view prediction reduces progress error by 41-69% compared with averaging single-view RGB predictions and improves temporal ordering in approximately 88% of task-camera groups. On real tasks with fixed and wrist-mounted cameras, mean absolute error decreases by approximately 21% relative to averaged RGB fine-tuning. These results support camera geometry and joint visual evidence as useful components of task-specific robotic reward adaptation.