Look, Listen, and Act: Towards Audio-Visual Embodied Navigation

Abstract: A crucial ability of mobile intelligent agents is to integrate the evidence from multiple sensory inputs in an environment and to make a sequence of actions to reach their goals. In this paper, we attempt to approach the problem of Audio-Visual Embodied Navigation, the task of planning the shortest path from a random starting location in a scene to the sound source in an indoor environment, given only raw egocentric visual and audio sensory data. To accomplish this task, the agent is required to learn from various modalities, i.e. relating the audio signal to the visual environment. Here we describe an approach to audio-visual embodied navigation that takes advantage of both visual and audio pieces of evidence. Our solution is based on three key ideas: a visual perception mapper module that constructs its spatial memory of the environment, a sound perception module that infers the relative location of the sound source from the agent, and a dynamic path planner that plans a sequence of actions based on the audio-visual observations and the spatial memory of the environment to navigate toward the goal. Experimental results on a newly collected Visual-Audio-Room dataset using the simulated multi-modal environment demonstrate the effectiveness of our approach over several competitive baselines.

• The paper introduces a multi-modal framework that integrates raw audio and visual data, enabling agents to locate sound sources without pre-mapped environments.
• It details a system architecture with a visual perception mapper, sound perception module, and dynamic path planner that collectively enhance spatial memory and routing efficiency.
• Validated on the VAR dataset, the approach outperforms vision-only and audio-only baselines, achieving higher success rates and SPL metrics for robust real-world applications.

Overview of "Look, Listen, and Act: Towards Audio-Visual Embodied Navigation"

The paper "Look, Listen, and Act: Towards Audio-Visual Embodied Navigation" by Gan et al., addresses the complex task of enabling intelligent agents to perform audio-visual embodied navigation. This task involves navigating an environment solely using raw, egocentric visual and audio sensory data to find a sound source, all without prior scene knowledge. This study demonstrates how mobile agents can integrate multi-modal sensory input, much like humans do, to perform actions and achieve targeted outcomes.

Key Concepts

1. Multi-Modal Sensory Integration: The paper emphasizes the integration of audio signals with visual environmental cues, a capability naturally robust in humans. This integration is pivotal for developing agents that can perform complex interaction tasks in novel environments.
2. System Architecture: The proposed navigation framework comprises three fundamental modules:
   • Visual Perception Mapper: Constructs spatial memory through visual observations. This module utilizes a partial graph representation for environments, aiding efficient navigation by memorizing explored sections.
   • Sound Perception Module: Determines the relative position of the sound source, supplying critical directional and distance information to guide the agent towards the goal.
   • Dynamic Path Planner: Leverages inputs from the other two modules to dynamically plan and update paths, thereby optimizing the trajectory to the sound source.
3. Problem Setup and Environment: Two setups are considered:
   • Explore-and-Act: The agent may explore the environment within a pre-defined budget of steps before the sound source activates. The knowledge gained is stored as a partial map, informing future actions.
   • Non-Exploration: In a more challenging setting, the agent builds its map in real-time as it navigates towards the active sound source.
4. Experimental Validation: The work is validated using a newly designed Visual-Audio Room (VAR) dataset, crafted to test the audio-visual navigation capabilities in complex, simulated apartments. This dataset contains challenging acoustic conditions to test the agent's robustness across unexpected environments.

Experimental Results

The experimental results highlight the efficacy of their approach:

• The success rate of navigation for the proposed framework considerably exceeds that of baseline methods, such as A3C-based approaches, showcasing the benefits of explicit spatial memory and multimodal integration.
• The SPL metrics indicate efficient routing and path selection, demonstrating substantial improvements over vision-only or audio-only navigation methods.

Implications and Future Directions

1. Practical Applications: This research paves the way for applications in domestic robots, where sound-source localization and navigation are crucial for assisting humans in daily tasks such as finding lost items.
2. Theoretical Implications: The approach underscores the importance of multi-modal learning for embodied AI, providing insights into how sensory integration can lead to better awareness and interaction within complex environments.
3. Future Work: Future research may focus on improving generalization to novel environments, enhancing the robustness against a wider variety of acoustic conditions, and integrating more sensory modalities to broaden the scope of navigation and interaction tasks feasible for embodied agents.

This paper presents significant advancements in the field of artificial intelligence by combining sensory inputs to improve agent-environment interaction and navigation capabilities. The proposed systems enhance the state-of-the-art in embodied AI and demonstrate the potential of multi-sensory learning frameworks.