Abstract
Tactile object recognition (TOR) is critical in robot perception. However, as an embedded system, a robot brain has a fixed resource budget and is unsuitable for modern convolutional neural networks (CNNs). To bridge this gap, we present a simple network-compression approach that improves the accuracy-latency trade-off of the network. The multi-trim network structure (MTNS) is a robust combination of network compression (NC) techniques providing a lightweight network with no performance drop. Furthermore, as an optical tactile sensor, we present a random-dot sensor that obtains rich information with a single touch, thus avoiding modality fusion. The random-dot sensor captures the object shapes and inputs them to TOR. In an experimental evaluation, we compare the performances of the proposed MTNS approach with those of CNN filter pruning, the network quantization technique, an adaptive mixture of low-rank factorizations, and knowledge distillation. The MTNS better resolved the accuracy-latency trade-off in tactile object recognition than the modern NC methods. By combining the random-dot sensor and MTNS approach, TOR enhances the accuracy and processing time performances.
Highlights
Much effort has gone into developing smart robots, wherein perception and manipulation are among the most fundamental and challenging problems
We present Trim Neural Architecture Search (TrimNAS), which is a macro search to compress networks to meet the trade-off between accuracy and latency
EXPERIMENTS ON SHRINKING NETWORK STRUCTURE 1) SEARCH SPACE we look for the compound multipliers that gave the highest score for the child networks
Summary
Much effort has gone into developing smart robots, wherein perception and manipulation are among the most fundamental and challenging problems. Results of the tactile image captured using random-dot OTS and compressed network perform a low latency and high accuracy. The MTNS approach involves compressing the network structure (i.e., shrinking the network, developing a training accelerator, and formulating a search strategy).
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