Visual Representations for Semantic Target Driven Navigation (1805.06066v3)

Abstract: What is a good visual representation for autonomous agents? We address this question in the context of semantic visual navigation, which is the problem of a robot finding its way through a complex environment to a target object, e.g. go to the refrigerator. Instead of acquiring a metric semantic map of an environment and using planning for navigation, our approach learns navigation policies on top of representations that capture spatial layout and semantic contextual cues. We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy. This choice allows using additional data, from orthogonal sources, to better train different parts of the model the representation extraction is trained on large standard vision datasets while the navigation component leverages large synthetic environments for training. This combination of real and synthetic is possible because equitable feature representations are available in both (e.g., segmentation and detection masks), which alleviates the need for domain adaptation. Both the representation and the navigation policy can be readily applied to real non-synthetic environments as demonstrated on the Active Vision Dataset [1]. Our approach gets successfully to the target in 54% of the cases in unexplored environments, compared to 46% for non-learning based approach, and 28% for the learning-based baseline.

• The paper leverages deep networks with high-level semantic features to achieve a 54% success rate in autonomous navigation in unknown environments.
• It employs a novel framework that bypasses traditional 3D mapping by using segmentation and detection masks to capture spatial and contextual cues.
• The research mitigates sim-to-real transfer issues through combined training on real and synthetic datasets, enhancing practical robotic applications.

Visual Representations for Semantic Target Driven Navigation

The paper "Visual Representations for Semantic Target Driven Navigation" explores the critical question of deriving optimal visual representations for the effective navigation of robots in previously unexplored environments. This exploration is contextualized within the framework of semantic visual navigation, a task wherein a robot autonomously finds a path to a semantically defined target object, such as a refrigerator. Rather than employing traditional methods that rely on 3D semantic mapping followed by path planning, the authors advocate for a novel approach that leverages deep networks trained on spatial layout and semantic contextual cues.

The method proposed involves the use of high-level semantic features derived from segmentation and detection masks. These features are obtained through the application of advanced computer vision algorithms. By employing a deep neural network, a navigation policy is learned directly from these representations. The effectiveness of this framework is predicated upon the availability of rich data from simulated environments, which allows for the joint training of models using both real and synthetic datasets. This mitigates the often challenging sim-to-real transfer issues without necessitating domain adaptation or domain randomization.

A substantial claim of this research is its improved navigational success rate. The proposed method achieves a 54% success rate in guiding the autonomous agent to the target in unknown environments, outperforming a non-learning based strategy that achieves 46% and a baseline learning-based approach achieving only 28%.

The paper makes several important contributions to the domain of robotic visual navigation:

• Semantic Representational Efficacy: The paper confirms that using semantically segmented data and object detection masks encapsulates sufficient detail for robust navigation, providing necessary spatial cues without needing a full RGB representation. This signals a shift in focus toward more abstracted yet detail-rich data forms for navigation contexts.
• Impact on Simulated Training: By concentrating on representation structures that work seamlessly in both real and simulated data, the authors demonstrate significant performance gains and unique insights into the adaptation gap problem typically encountered in robotic training paradigms.
• Training with Reinforcement Signals: Strong supervision through simulated environments employing path planning algorithms—a.k.a. stronger supervision—facilitates an effective framework for training navigation policies. This approach elucidates the progress metric used to guide the navigation decisions taken at each step.
• Architectural Analysis: The application of RNN components, incorporating LSTM networks into the proposed architecture, exhibits notable improvements in environments that see mixed real and simulated data.

The implications of this research span both theoretical understanding and practical settings. Practically, the deployment of such navigation systems within real-world robotic applications offering more efficient, scalable navigation systems is foreseeable. Theoretically, the findings advise on the richness of information conveyed through semantic and contextual cues over traditional raw data, thereby informing future developments and optimizations in autonomous navigational design principles.

In conclusion, the investigation into semantic target driven navigation presents an important stride forward, emphasizing the unexploited potential of semantic data in conjunction with advanced neural architectures. Future research directions may explore enhancing the intricacy of semantic representations and further minimizing the disparity between simulated training environments and their real-world counterparts, promising escalating advancements in robotic autonomy.