Towards smart and adaptive agents for active sensing on edge devices

TL;DR

This paper introduces an on-device active sensing system using active inference principles, enabling adaptive perception and planning on resource-constrained edge devices like NVIDIA Jetson, surpassing traditional deep learning limitations.

Contribution

The paper presents a novel agentic system that combines active inference with edge computing for real-time adaptive perception and planning, with a compact memory footprint.

Findings

Successfully deployed a saccade agent on NVIDIA Jetson

Demonstrated real-time adaptive camera control in dynamic environments

Achieved human-like saccadic motion for surveillance applications

Abstract

TinyML has made deploying deep learning models on low-power edge devices feasible, creating new opportunities for real-time perception in constrained environments. However, the adaptability of such deep learning methods remains limited to data drift adaptation, lacking broader capabilities that account for the environment's underlying dynamics and inherent uncertainty. Deep learning's scaling laws, which counterbalance this limitation by massively up-scaling data and model size, cannot be applied when deploying on the Edge, where deep learning limitations are further amplified as models are scaled down for deployment on resource-constrained devices. This paper presents an innovative agentic system capable of performing on-device perception and planning, enabling active sensing on the edge. By incorporating active inference into our solution, our approach extends beyond deep learning capabilities, allowing the system to plan in dynamic environments while operating in real-time with a compact memory footprint of as little as 300 MB. We showcase our proposed system by creating and deploying a saccade agent connected to an IoT camera with pan and tilt capabilities on an NVIDIA Jetson embedded device. The saccade agent controls the camera's field of view following optimal policies derived from the active inference principles, simulating human-like saccadic motion for surveillance and robotics applications.