# Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling

arXiv: https://arxiv.org/abs/2010.08596 · HTML: https://mlsanity.com/paper/2010.08596/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [UAV-ON: A Benchmark for Open-World Object Goal Navigation with Aerial Agents](https://mlsanity.com/paper/2508.00288/) (2025)
- [Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge](https://mlsanity.com/paper/2109.09531/) (2021)
- [Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph](https://mlsanity.com/paper/2103.01350/) (2021)
- [Smart Explorer: Recognizing Objects in Dense Clutter via Interactive Exploration](https://mlsanity.com/paper/2208.03496/) (2022)
- [Distilling LLM Prior to Flow Model for Generalizable Agent's Imagination in Object Goal Navigation](https://mlsanity.com/paper/2508.09423/) (2025)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)
- [AERR-Nav: Adaptive Exploration-Recovery-Reminiscing Strategy for Zero-Shot Object Navigation](https://mlsanity.com/paper/2603.17712/) (2026)
- [EvoMemNav: Efficient Self-Evolving Fine-Grained Memory for Zero-Shot Embodied Navigation](https://mlsanity.com/paper/2606.03509/) (2026)
- [EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigation](https://mlsanity.com/paper/2606.18235/) (2026)

## Abstract (arXiv)

Despite the significant success at enabling robots with autonomous behaviors makes deep reinforcement learning a promising approach for robotic object search task, the deep reinforcement learning approach severely suffers from the nature sparse reward setting of the task. To tackle this challenge, we present a novel policy learning paradigm for the object search task, based on hierarchical and interpretable modeling with an intrinsic-extrinsic reward setting. More specifically, we explore the environment efficiently through a proxy low-level policy which is driven by the intrinsic rewarding sub-goals. We further learn our hierarchical policy from the efficient exploration experience where we optimize both of our high-level and low-level policies towards the extrinsic rewarding goal to perform the object search task well. Experiments conducted on the House3D environment validate and show that the robot, trained with our model, can perform the object search task in a more optimal and interpretable way.