轻量化神经网络让边缘设备实时精准抓取物体
A Segmented Robot Grasping Perception Neural Network for Edge AI
- 用热图引导的端到端框架检测6自由度抓取姿态
- 在GAP9芯片上实现全芯片推理，延迟低功耗小
- 适合资源受限场景的自主机器人抓取任务
机器人抓取需精准感知与控制，深度神经网络通过学习物体抽象表征显著提升抓取性能。本文将热图引导的抓取检测框架部署于GAP9 RISC-V片上系统，在边缘端实现6自由度抓取姿态的端到端检测。通过输入降维、模型分块与量化等硬件感知优化技术，显著降低计算开销。在GraspNet-1Billion基准上的实验验证了全芯片推理的可行性，证明低功耗MCU具备实时自主操控潜力。
原文摘要 · Abstract (English)
Robotic grasping, the ability of robots to reliably secure and manipulate objects of varying shapes, sizes and orientations, is a complex task that requires precise perception and control. Deep neural networks have shown remarkable success in grasp synthesis by learning rich and abstract representations of objects. When deployed at the edge, these models can enable low-latency, low-power inference, making real-time grasping feasible in resource-constrained environments. This work implements Heatmap-Guided Grasp Detection, an end-to-end framework for the detection of 6-Dof grasp poses, on the GAP9 RISC-V System-on-Chip. The model is optimised using hardware-aware techniques, including input dimensionality reduction, model partitioning, and quantisation. Experimental evaluation on the GraspNet-1Billion benchmark validates the feasibility of fully on-chip inference, highlighting the potential of low-power MCUs for real-time, autonomous manipulation.
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