semantic-audio-visual-navigation

name	semantic-audio-visual-navigation
title	Semantic Audio-Visual Navigation in Continuous Environments
version	0.0.3
engine	skillxiv-v0.0.3-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2603.19660
keywords	["Embodied AI","Audio-Visual Navigation","Multimodal Fusion","Continuous Control","Goal Tracking"]
description	Enable agents to navigate toward sound-emitting objects in continuous 3D spaces with dynamic audio (intermittent sounds, silent periods). Integrate memory-augmented goal descriptors with binaural audio processing and self-motion cues to maintain goal representations even after auditory signals cease.

Semantic Audio-Visual Navigation in Continuous Environments

Problem Statement & Task Definition

SAVN-CE Task: Navigate embodied agents toward intermittently-emitting sound sources in continuous 3D indoor environments with realistic audio rendering.

Key Complexity: Unlike prior discrete-grid navigation, agents move with fine-grained continuous actions (0.25m translations, 15° rotations) and must track goals through silence periods when audio signals vanish.

Realism: Uses real-time binaural audio rendering instead of precomputed impulse responses—agents hear dynamic acoustic cues as they move through space.

Component Innovation: Memory-Augmented Goal Descriptor

The Modification: Extend standard visual navigation to continuous environments by adding memory-augmented goal descriptor network (MAGNet) that fuses audio, self-motion, and episodic memory for robust goal tracking during silence.

Three-Component Architecture:

1. Multimodal Observation Encoder:

   • RGB-D image processing
   • Binaural waveform audio features
   • Agent egomotion (velocity, angular velocity)
   • Previous action history

2. Memory-Augmented Goal Descriptor Network (GDN):

   • Accumulates auditory cues across time steps
   • Integrates self-motion to predict goal location during silence
   • Maintains episodic memory of past audio observations
   • Outputs ACCDDOA representation:
     • Activity: Is goal currently emitting?
     • Direction-of-Arrival (DoA): Angular direction to goal
     • Distance: Estimated distance to goal

# Goal descriptor memory mechanism:
# Maintains goal state g_t combining:
# - Current audio cue a_t (binaural waveform features)
# - Self-motion delta: Δx_t, Δθ_t (translation + rotation)
# - Episodic memory: M = {(a_τ, x_τ) for τ < t}
#
# Memory-updated goal estimate:
# g_t = GDN(a_t, Δx_t, Δθ_t, M)
# → outputs (activity, direction_of_arrival, distance)

3. Context-Aware Policy Network:
   • Transformer-based decoder
   • Conditions on goal descriptor
   • Generates continuous navigation actions

What Was Modified for Continuous Environments

Discretization Removal: Prior work used 0.5m grid steps; SAVN-CE uses 0.25m continuous movement with smooth rotation (15° granularity).

Audio Representation: Real-time binaural audio instead of pre-rendered impulse responses—agents receive dynamic acoustic updates reflecting position changes.

Goal Tracking Strategy: Active memory accumulation—system maintains running estimate of goal location even when silent, updated via:

• Audio cues when available (strong signal)
• Self-motion integration when silent (dead-reckoning)
• Episodic memory fusion (comparing current cues to past observations)

Experimental Results

Success Rates (Clean Environments):

• SAVN-CE with MAGNet: 37.7% (baseline from prior discrete methods: 25.6%)
• 12.1% absolute improvement in success rate
• Superior robustness to short-duration sounds and long-distance goals

Key Scenarios:

• Continuous Audio: 48.2% success (best-case)
• **Intermittent Audio (5s on, 5s silent): 37.7% success
• **Very Short Sounds (1s bursts): 22.4% success

Challenging Condition (Mixed Sounds): Performance degrades to 18.3% with competing distractor sounds, indicating room for improved distraction handling.

Dataset & Evaluation

• Environment: Matterport3D with 500K training episodes
• Semantic Categories: 21 object types (chair, table, doorway, kitchen, etc.)
• Distractor Sounds: 102 periodic distractor sounds mixed with goal audio
• Evaluation Metric: Success rate = agent reaches goal within 0.5m and orients correctly

Deployment Considerations

1. Audio Rendering Fidelity: Real-time binaural rendering requires reasonable spatial audio simulation; pre-rendered IRs may suffice in practice but lose dynamic updates.

2. Continuous Action Mapping: Map model outputs to robot actuators; 0.25m movements appropriate for humanoid robots, smaller increments for wheeled platforms.

3. Memory Capacity: MAGNet maintains episodic memory; limit window to last 20-30 observations to avoid unbounded memory growth.

4. Distractor Robustness: Current system struggles with competing sounds; mitigate by:

   • Semantic filtering (goal category labels reduce ambiguity)
   • Temporal coherence (favor consistent directions over jittering)
   • Multi-hypothesis tracking (maintain multiple goal candidates)

5. Silence Duration Tolerance: System maintains goal estimates for ~30-60 seconds of silence; beyond that, dead-reckoning diverges and performance collapses.

Practical Implications

• Bridges Simulation-to-Real Gap: Continuous control and realistic audio move beyond toy gridworld navigation
• Multimodal Fusion Value: Memory-augmented architecture shows 12% improvement—memory integration critical for dynamic audio
• Scalability: Tested on indoor environments; outdoor navigation (GPS-less) feasible but untested