Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality (2210.01683v3)

Abstract: For the best human-robot interaction experience, the robot's navigation policy should take into account personal preferences of the user. In this paper, we present a learning framework complemented by a perception pipeline to train a depth vision-based, personalized navigation controller from user demonstrations. Our virtual reality interface enables the demonstration of robot navigation trajectories under motion of the user for dynamic interaction scenarios. The novel perception pipeline enrolls a variational autoencoder in combination with a motion predictor. It compresses the perceived depth images to a latent state representation to enable efficient reasoning of the learning agent about the robot's dynamic environment. In a detailed analysis and ablation study, we evaluate different configurations of the perception pipeline. To further quantify the navigation controller's quality of personalization, we develop and apply a novel metric to measure preference reflection based on the Fr\'echet Distance. We discuss the robot's navigation performance in various virtual scenes and demonstrate the first personalized robot navigation controller that solely relies on depth images. A supplemental video highlighting our approach is available online.

• The paper introduces a novel framework that leverages depth vision and dynamic VR demonstrations to learn personalized robot navigation policies.
• It integrates a VAE for efficient state compression and an LSTM-based motion predictor within a hybrid reinforcement learning and behavioral cloning architecture.
• Evaluation with Fréchet distance metrics demonstrates improved replication of demonstrated trajectories and robust navigation in dynamic VR scenarios.

An Examination of Depth Vision-Based Personalized Robot Navigation through Dynamic Demonstrations

The paper presented provides an in-depth exploration of a novel framework designed for learning personalized robot navigation policies, leveraging dynamic user demonstrations in virtual reality (VR) settings. Recognizing that the integration of robots into everyday environments is increasing, there is a pertinent need to align robot navigation behavior with individual human preferences. This paper introduces a model that employs depth vision to train a personalized navigation controller, addressing this need by effectively bridging human interaction nuances with autonomous navigation systems.

Core Contributions and Methodology

The paper primarily contributes to robotic navigation by introducing a depth vision-based perception pipeline integrated with a learning framework. This configuration is adept at processing dynamic user demonstrations facilitated within a VR interface. Key elements of the approach include:

1. Perception Pipeline: The authors employ a variational autoencoder (VAE) to compress high-dimensional depth images into a low-dimensional latent state, which aids efficient learning even in complex and dynamically changing environments. Complementing the VAE is an LSTM-based motion predictor that forecasts future states, granting the system foresight in navigating dynamic scenes with moving humans.
2. Reinforcement Learning Architecture: A hybrid learning architecture combines reinforcement learning (RL) with behavioral cloning. This dual approach allows leveraging user demonstration data to guide policy learning, ensuring that the robot's navigation adheres to personalized user preferences. The use of TD3 RL architecture facilitates stable learning in continuous action spaces typical of robotic control scenarios.
3. Evaluation via Novel Metrics: To measure the effectiveness in reflecting navigation preferences, the paper develops a metric based on the Fréchet distance, tailored to capture the extent of preference reflection in navigation tasks. This metric evaluates how closely the learned navigation policies match the demonstrated trajectories, providing a nuanced indication of the system's personalization capability.

Results and Analysis

The system is validated across various VR scenarios, evaluating configurations for their ability to replicate demonstrated navigation preferences accurately. The analysis shows:

• Preference Reflection: The VAE-enabled perception model adeptly reflects user preferences as demonstrated in VR, excelling particularly over controllers devoid of demonstration data.
• Robustness and Adaptability: Controllers trained with the proposed approach show commendable robustness, maintaining high success rates across varied and complex environmental conditions, even as human agents within the scenes change behavior.
• Comparison Across Configurations: The paper conducts an exhaustive comparison across different perception and learning configurations, revealing that pure VAE-based approaches offer the best trade-off between model complexity and preference replication accuracy. Notably, configurations utilizing the LSTM predictor did not significantly outperform simpler VAE-only approaches, suggesting potential areas for future exploration in optimizing predictive state representations.

Implications and Future Prospects

The implications of this research are multifaceted, providing both practical advancements in personalizing robot navigation and pushing theoretical boundaries in perception-based learning models. By exploiting depth vision, the researchers propose a cost-effective and scalable solution for personalized navigation — a critical advancement given the increasing deployment of robots in homes and public spaces.

Future research could explore integrating richer sensory inputs, such as multimodal data combining depth with optical image data, to enhance prediction confidence and policy performance. Furthermore, expanding the scope of environments and the diversity of user preferences sampled could enhance generalization in real-world settings.

In conclusion, this paper lays substantive groundwork in crafting navigation policies that resonate closely with human intent, harnessing the potential of perception-driven RL frameworks. This is crucial for fostering intuitive and user-aligned autonomous systems that not only navigate efficiently but also enrich human-robot interaction with a personalized touch.