# Learning Long-Horizon Robot Exploration Strategies for Multi-Object Search in Continuous Action Spaces

arXiv: https://arxiv.org/abs/2205.11384 · HTML: https://mlsanity.com/paper/2205.11384/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2022.

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

- [GaussNav: Gaussian Splatting for Visual Navigation](https://mlsanity.com/paper/2403.11625/) (2024)
- [DiWA: Diffusion Policy Adaptation with World Models](https://mlsanity.com/paper/2508.03645/) (2025)
- [N2M2: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments](https://mlsanity.com/paper/2206.08737/) (2022)
- [Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation](https://mlsanity.com/paper/2307.06125/) (2023)
- [AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving](https://mlsanity.com/paper/2309.06547/) (2023)
- [CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation](https://mlsanity.com/paper/2312.08240/) (2023)
- [The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation](https://mlsanity.com/paper/2305.04718/) (2023)
- [A System for Generalized 3D Multi-Object Search](https://mlsanity.com/paper/2303.03178/) (2023)
- [MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning](https://mlsanity.com/paper/2505.03035/) (2025)
- [Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation](https://mlsanity.com/paper/2408.02297/) (2024)
- [Find Everything: A General Vision Language Model Approach to Multi-Object Search](https://mlsanity.com/paper/2410.00388/) (2024)
- [Automating Deformable Gasket Assembly](https://mlsanity.com/paper/2408.12593/) (2024)
- [Relational Semantic Reasoning on 3D Scene Graphs for Open World Interactive Object Search](https://mlsanity.com/paper/2603.05642/) (2026)
- [The Neural Compass: Probabilistic Relative Feature Fields for Robotic Search](https://mlsanity.com/paper/2603.08544/) (2026)

## Abstract (arXiv)

Recent advances in vision-based navigation and exploration have shown impressive capabilities in photorealistic indoor environments. However, these methods still struggle with long-horizon tasks and require large amounts of data to generalize to unseen environments. In this work, we present a novel reinforcement learning approach for multi-object search that combines short-term and long-term reasoning in a single model while avoiding the complexities arising from hierarchical structures. In contrast to existing multi-object search methods that act in granular discrete action spaces, our approach achieves exceptional performance in continuous action spaces. We perform extensive experiments and show that it generalizes to unseen apartment environments with limited data. Furthermore, we demonstrate zero-shot transfer of the learned policies to an office environment in real world experiments.