Object Goal Navigation using Goal-Oriented Semantic Exploration (2007.00643v2)

Abstract: This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. End-to-end learning-based navigation methods struggle at this task as they are ineffective at exploration and long-term planning. We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category. Empirical results in visually realistic simulation environments show that the proposed model outperforms a wide range of baselines including end-to-end learning-based methods as well as modular map-based methods and led to the winning entry of the CVPR-2020 Habitat ObjectNav Challenge. Ablation analysis indicates that the proposed model learns semantic priors of the relative arrangement of objects in a scene, and uses them to explore efficiently. Domain-agnostic module design allow us to transfer our model to a mobile robot platform and achieve similar performance for object goal navigation in the real-world.

• The paper demonstrates that integrating semantic mapping with episodic memory significantly enhances navigation efficiency by strategically directing exploration.
• The authors employ a modular approach that outperforms traditional end-to-end and modular methods in key metrics like success rate and SPL.
• The method’s domain-agnostic design enables robust performance across both simulated environments and real-world mobile robotic platforms.

Overview of Goal-Oriented Semantic Exploration

The paper "Object Goal Navigation using Goal-Oriented Semantic Exploration" by Chaplot et al. presents an innovative approach to object goal navigation, a fundamental problem in autonomous navigation for embodied agents. The task involves directing an agent to locate a specific object instance within unknown environments based on a given object category. The paper identifies limitations in end-to-end learning models and existing modular systems and proposes a novel technique that enhances navigation efficiency by integrating semantic priors and episodic memory into the navigation process.

Problem Background

Object goal navigation involves not only recognizing an object but also understanding spatial layouts to infer likely object locations—a challenge exacerbated by unknown environments. Traditional end-to-end models, such as those leveraging reinforcement or imitation learning, often underperform due to large sample complexity and inadequate generalization beyond training environments, owing to their reliance on memorized object features rather than learned spatial heuristics.

Contribution of the Paper

The proposed "Goal-Oriented Semantic Exploration" (SemExp) offers a modular strategy that utilizes an episodic semantic map, created during exploration, to potentiate goal-directed navigation. This method is shown to outperform both end-to-end methods and existing modular approaches significantly, effectively navigating environments by utilizing a combination of learned semantics and explicit mapping.

Key Components

1. Semantic Mapping: SemExp constructs maps that incorporate both metric measurements and semantic categories by employing a sophisticated projection technique. This allows a precise understanding of spatial arrangements and object distributions, enhancing navigational efficiency.
2. Goal-Oriented Semantic Policy: This feature leverages learned semantic priors to select exploration goals dynamically. This learned policy prioritizes regions in an environment where the target object category is statistically more probable, thereby optimizing the agent’s exploration path.
3. Domain-Agnostic Design: The modular framework is designed to be domain-agnostic, which allows the model to transition seamlessly from simulated environments to real-world scenarios. This characteristic was validated by demonstrating comparable performance on a mobile robotic platform.

Numerical Results

The empirical evaluation conducted within realistic simulation environments showed significant performance improvements over existing benchmarks. SemExp succeeded in achieving higher success rates and better SPL (Success weighted by Path Length) compared to both end-to-end learning policies and classical mapping with frontier-based exploration. The proposed model also emerged as the top performer in the CVPR 2020 Habitat ObjectNav Challenge.

Theoretical and Practical Implications

The integration of semantic information into map-based navigation introduces a paradigm shift in how autonomous navigation systems can approach exploration in novel environments. This advancement underscores the potential of combining explicit map representations with semantic awareness to open new avenues for research in embodied AI, especially for tasks necessitating strategic long-term planning in complex environments.

Future Directions

Further exploration could focus on enhancing the model's semantic segmentation capabilities, as error analysis indicates current performance is partly constrained by these limitations. Additionally, extending the model’s capabilities to handle dynamic environments or sequences of object goals could provide a more robust framework for autonomous agents operating in real-world scenarios.

Overall, Goal-Oriented Semantic Exploration establishes a compelling framework that aligns semantic understanding and spatial reasoning, essential for next-generation intelligent embodied agents. This paper sets a new standard for efficiency and adaptability in object goal navigation, showcasing the merit of modular and semantically-grounded approaches.