# Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning

arXiv: https://arxiv.org/abs/2203.02381 · HTML: https://mlsanity.com/paper/2203.02381/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2022.

## Built on by (most cited first, 14 of 14)

- [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://mlsanity.com/paper/2205.03716/) (2022)
- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Learning Continuous Control Policies for Information-Theoretic Active Perception](https://mlsanity.com/paper/2209.12427/) (2022)
- [Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information](https://mlsanity.com/paper/2407.15593/) (2024)
- [Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories](https://mlsanity.com/paper/2212.01498/) (2022)
- [Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach](https://mlsanity.com/paper/2310.02650/) (2023)
- [Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction](https://mlsanity.com/paper/2307.05832/) (2023)
- [ActLoc: Learning to Localize on the Move via Active Viewpoint Selection](https://mlsanity.com/paper/2508.20981/) (2025)
- [OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots](https://mlsanity.com/paper/2603.11072/) (2026)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)
- [GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments](https://mlsanity.com/paper/2307.04019/) (2023)
- [NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points](https://mlsanity.com/paper/2503.02111/) (2025)
- [Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games](https://mlsanity.com/paper/2504.07425/) (2025)
- [Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration](https://mlsanity.com/paper/2605.22814/) (2026)

## Abstract (arXiv)

Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.