Cross-Modal Navigation with Multi-Agent Reinforcement Learning

Problem
This paper addresses the challenge of robust embodied navigation using multi-modal sensory inputs, which is often hindered by the difficulty of obtaining high-quality, well-aligned multi-modal data. The authors highlight the limitations of training monolithic models that struggle with complex representations and an enlarged policy space due to rich multi-modal inputs. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose CRONA, a Multi-Agent Reinforcement Learning (MARL) framework designed for Cross-Modal Navigation. CRONA employs a decentralized approach where lightweight, modality-specialized agents collaborate to enhance navigation performance. The framework incorporates control-relevant auxiliary beliefs and utilizes a centralized multi-modal critic that processes global state information. This architecture allows for flexible deployment and parallel execution of agents, optimizing the strengths of each modality. The training process leverages a combination of reinforcement learning techniques, although specific details regarding the loss functions, training compute, and dataset specifics are not disclosed.

Results
CRONA demonstrates significant improvements in navigation tasks compared to single-agent baselines. The authors report that homogeneous collaboration among agents with limited modalities is effective for short-range navigation when salient cues are present. In contrast, heterogeneous collaboration, where agents utilize complementary modalities, yields superior performance in more complex environments. The paper provides quantitative results indicating that CRONA outperforms baseline methods on visual-acoustic navigation tasks, although specific performance metrics (e.g., success rates, efficiency gains) are not detailed in the summary.

Limitations
The authors acknowledge that while CRONA shows promise, there are limitations in its current implementation. They note that the framework’s effectiveness may diminish in environments that require richer multi-modal perception and increased model capacity. Additionally, the reliance on centralized critics may introduce bottlenecks in scalability and robustness. The paper does not address potential issues related to the computational overhead of managing multiple agents or the challenges of real-world deployment, such as sensor noise and dynamic environments.

Why it matters
The implications of this work are significant for the field of embodied AI and multi-modal learning. By demonstrating the efficacy of multi-agent collaboration in navigation tasks, CRONA paves the way for more scalable and efficient systems that can leverage diverse sensory inputs. This approach could enhance applications in robotics, autonomous vehicles, and virtual agents, where robust navigation in complex environments is critical. The findings encourage further exploration of multi-agent frameworks and their potential to address the limitations of traditional monolithic models in multi-modal settings.

Authors: Shuo Liu, Xinzichen Li, Christopher Amato
Source: arXiv:2605.06595
URL: https://arxiv.org/abs/2605.06595v1