Enhancing Continuous Control of Mobile Robots for End-to-End Visual Active Tracking

TL;DR

This paper introduces a novel deep reinforcement learning system for continuous control in visual active tracking, improving training efficiency and performance, and demonstrating successful real-world application from simulation training.

Contribution

The work presents a new DRL-based active tracking method with continuous actions, enhanced training techniques, and a heuristic generator, surpassing existing approaches.

Findings

Outperforms state-of-the-art methods in active tracking tasks.

Effective transfer from simulation to real-world scenarios.

Improved training speed and stability with new objectives and heuristics.

Abstract

In the last decades, visual target tracking has been one of the primary research interests of the Robotics research community. The recent advances in Deep Learning technologies have made the exploitation of visual tracking approaches effective and possible in a wide variety of applications, ranging from automotive to surveillance and human assistance. However, the majority of the existing works focus exclusively on passive visual tracking, i.e., tracking elements in sequences of images by assuming that no actions can be taken to adapt the camera position to the motion of the tracked entity. On the contrary, in this work, we address visual active tracking, in which the tracker has to actively search for and track a specified target. Current State-of-the-Art approaches use Deep Reinforcement Learning (DRL) techniques to address the problem in an end-to-end manner. However, two main problems arise: i) most of the contributions focus only on discrete action spaces and the ones that consider continuous control do not achieve the same level of performance; and ii) if not properly tuned, DRL models can be challenging to train, resulting in a considerably slow learning progress and poor final performance. To address these challenges, we propose a novel DRL-based visual active tracking system that provides continuous action policies. To accelerate training and improve the overall performance, we introduce additional objective functions and a Heuristic Trajectory Generator (HTG) to facilitate learning. Through an extensive experimentation, we show that our method can reach and surpass other State-of-the-Art approaches performances, and demonstrate that, even if trained exclusively in simulation, it can successfully perform visual active tracking even in real scenarios.