Edge-Native Temporal--Graph Dual-Expert Inference for Dexterous Grasp Classification

Key points

• Multi-finger hands contact objects at different times, so a global close command can overload early-contact fingers while others are still moving.1
• Dense tactile arrays address this problem but add hardware, calibration, bandwidth, and computation.1
• At its core is a heterogeneous dual-expert architecture, termed DualExpert, that assigns complementary tasks to a Temporal Convolutional Network (TCN) and a Finger Graph Filter Network (FGFN).1
• The TCN models short-term contact evolution and supplies all five Stop probabilities, while the FGFN encodes finger topology and contributes only to object classification.1

Sources (1)

• [1]Edge-Native Temporal--Graph Dual-Expert Inference for Dexterous Grasp Classification
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 01:58 PM

  We present an edge-native pipeline that uses only built-in joint-angle and pre-use-calibrated actuator-load feedback to recognize the grasped object and predict an independent Stop event for each finger.

  Multi-finger hands contact objects at different times, so a global close command can overload early-contact fingers while others are still moving.

Extractive summary: sentences quoted from the sources.