# Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories

arXiv: https://arxiv.org/abs/2212.01498 · HTML: https://mlsanity.com/paper/2212.01498/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/2109.02869/) (2021)
- [Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.10423/) (2020)
- [Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly](https://mlsanity.com/paper/1903.01066/) (2019)
- [Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs](https://mlsanity.com/paper/2007.12640/) (2020)
- [Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning](https://mlsanity.com/paper/1810.04244/) (2018)
- [Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning](https://mlsanity.com/paper/2203.02381/) (2022)
- [Scalable Reinforcement Learning Policies for Multi-Agent Control](https://mlsanity.com/paper/2011.08055/) (2020)
- [Information Acquisition with Sensing Robots: Algorithms and Error Bounds](https://mlsanity.com/paper/1309.5390/) (2013)
- [Learning Continuous Control Policies for Information-Theoretic Active Perception](https://mlsanity.com/paper/2209.12427/) (2022)
- [Learning Q-network for Active Information Acquisition](https://mlsanity.com/paper/1910.10754/) (2019)
- [Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams](https://mlsanity.com/paper/2101.11093/) (2021)
- [Active Mapping via Gradient Ascent Optimization of Shannon Mutual Information over Continuous SE(3) Trajectories](https://mlsanity.com/paper/2204.07623/) (2022)
- [Active Exploration and Mapping via Iterative Covariance Regulation over Continuous SE(3) Trajectories](https://mlsanity.com/paper/2103.05819/) (2021)
- [Active SLAM over Continuous Trajectory and Control: A Covariance-Feedback Approach](https://mlsanity.com/paper/2110.07546/) (2021)

## Built on by (most cited first, 3 of 3)

- [Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment](https://mlsanity.com/paper/2306.11301/) (2023)
- [MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy](https://mlsanity.com/paper/2511.11931/) (2025)
- [Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance](https://mlsanity.com/paper/2607.22667/) (2026)

## Abstract (arXiv)

This paper proposes a novel model-based policy gradient algorithm for tracking dynamic targets using a mobile robot, equipped with an onboard sensor with limited field of view. The task is to obtain a continuous control policy for the mobile robot to collect sensor measurements that reduce uncertainty in the target states, measured by the target distribution entropy. We design a neural network control policy with the robot SE(3) pose and the mean vector and information matrix of the joint target distribution as inputs and attention layers to handle variable numbers of targets. We also derive the gradient of the target entropy with respect to the network parameters explicitly, allowing efficient model-based policy gradient optimization.