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

arXiv: https://arxiv.org/abs/2009.13475 · HTML: https://mlsanity.com/paper/2009.13475/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [TrackVLA: Embodied Visual Tracking in the Wild](https://mlsanity.com/paper/2505.23189/) (2025)
- [Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL](https://mlsanity.com/paper/2404.09857/) (2024)
- [TrackVLA++: Unleashing Reasoning and Memory Capabilities in VLA Models for Embodied Visual Tracking](https://mlsanity.com/paper/2510.07134/) (2025)
- [RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking](https://mlsanity.com/paper/2304.03623/) (2023)
- [Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers](https://mlsanity.com/paper/2405.12424/) (2024)
- [VLM Can Be a Good Assistant: Enhancing Embodied Visual Tracking with Self-Improving Vision-Language Models](https://mlsanity.com/paper/2505.20718/) (2025)
- [Open-World Drone Active Tracking with Goal-Centered Rewards](https://mlsanity.com/paper/2412.00744/) (2024)
- [Instance-level Visual Active Tracking with Occlusion-Aware Planning](https://mlsanity.com/paper/2604.21453/) (2026)
- [Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking](https://mlsanity.com/paper/2410.14685/) (2024)
- [CST-WM: A Causally Structured World Model for Embodied Visual Tracking](https://mlsanity.com/paper/2609.06302/) (2026)
- [Provably Efficient Reinforcement Learning in Continuous-Time Episodic MDPs with Poisson Decision Epochs](https://mlsanity.com/paper/2609.23127/) (2026)

## Abstract (arXiv)

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.