aiqbench analysis
The paper proposes a neural approach for robotic hands: a Temporal Convolutional Network and a graph-based network split the work of predicting per-finger stop events and recognizing grasped objects, using only joint-angle and actuator-load signals rather than dense tactile sensors. The results are evaluated on a 1,500-trajectory dataset from five objects, with 50 physical trials, and the embedded deployment reports about 2.9 ms inference and roughly 0.8 W power draw. For ML readers, the useful takeaways are the task-specialized dual-expert design and the low-cost sensing setup, though the object set is small and the physical trial count is modest.
Edge-Native Temporal--Graph Dual-Expert Inference for Dexterous Grasp Classification
arXiv:2610.14926v1 Announce Type: new Abstract: Multi-finger hands contact objects at different times, so a global close command can overload early-contact fingers while others are still moving. Dense tactile arrays address this problem but add hardware, calibration, bandwidth, and computation. 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. Four samples and their first differences form a 4-by-20 contact-evolution window. 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). 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. Fixed 0.7/0.3 class-probability fusion reaches 93.78\% trajectory-level classification accuracy on 225 held-out trials, improving the standalone TCN and FGFN by 4.00 and 2.22 percentage points, respectively. The TCN Stop path attains 95.25\% F1. The dataset contains 1,500 automatically collected trajectories from five object instances. In Flash deployment, mixed accelerator execution retains 93.78\% classification accuracy and 94.84\% Stop F1 with 2.890~ms mean inference time and 9.172~ms mean sample-to-stop latency. Across 50 physical trials, the system achieves 96.00\% recognition and 98.00\% grasp success while the complete embedded board draws approximately 0.80~W.
Abstract:Multi-finger hands contact objects at different times, so a global close command can overload early-contact fingers while others are still moving. Dense tactile arrays address this problem but add hardware, calibration, bandwidth, and computation. 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. Four samples and their first differences form a 4-by-20 contact-evolution window. 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). 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. Fixed 0.7/0.3 class-probability fusion reaches 93.78\% trajectory-level classification accuracy on 225 held-out trials, improving the standalone TCN and FGFN by 4.00 and 2.22 percentage points, respectively. The TCN Stop path attains 95.25\% F1. The dataset contains 1,500 automatically collected trajectories from five object instances. In Flash deployment, mixed accelerator execution retains 93.78\% classification accuracy and 94.84\% Stop F1 with 2.890~ms mean inference time and 9.172~ms mean sample-to-stop latency. Across 50 physical trials, the system achieves 96.00\% recognition and 98.00\% grasp success while the complete embedded board draws approximately 0.80~W.
Robotics (cs.RO)
arXiv:2610.14926 [cs.RO]
(or arXiv:2610.14926v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.14926 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 306 words
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