視覚触覚による物体回転分類のための最小光流表現：重力環境を超えたロボットマニピュレーション
A Minimal Optical-Flow Representation for Vision-Based Tactile Rotation Classification in Robotic Manipulation Across Gravity Domains
シミュレーション触覚センサの光流を126次元に圧縮し、地球・火星・月・軌道の重力下で物体回転方向を分類。全重力域で訓練した単一モデルは96.3%の精度を達成し、地球のみの訓練では軌道で75.9%に低下することを示した。
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著者: Oscar Martinez-Bernal, Mario Cavero-Vidal, Francesco Grella, Carol Martinez
分類: cs.RO, cs.CV
原文アブストラクト
Vision-based tactile sensors provide rich contact information, but processing high-resolution images can be costly for resource-constrained platforms such as space robots. This work investigates whether a compact representation of tactile motion can classify object rotation across different gravity conditions. Dense optical flow from a simulated GelSight Mini is aggregated over a 7x9 grid into 126 features and used to classify the direction of load-induced rotation under Earth, Mars, Moon, and orbital gravity. Gravity causes a small but significant shift in these features, accounting for 1.6% of their variance (R2 = 0.016). Despite its small magnitude, this shift affects models trained only on Earth data: XGBoost accuracy decreases from 94.4% on Earth to 75.9% in orbit. In contrast, a single model trained across all four gravity domains achieves 96.3% overall accuracy and 95.1%-97.0% across individual domains, without using gravity as an input. The representation can also be reduced to 40 features while retaining 95.7% accuracy, with XGBoost requiring only 0.14 ms per inference. These findings show that Earth-gravity performance alone is insufficient to establish the transferability of tactile perception for space robotic manipulation, highlighting the need to account for gravity-induced domain shifts during training and validation.