Distributed Learning of Neural Radiance Fields for Collaborative Multi-Robot Perception

Concetti Chiave

Multiple robotic agents can collaboratively learn a comprehensive neural radiance field (NeRF) representation of a scene by sharing only their learned network weights, without transferring raw sensor data, enabling efficient multi-agent perception.

Sintesi

The paper proposes a distributed learning framework for collaborative multi-robot perception using neural radiance fields (NeRFs). In this approach, each robotic agent processes its local sensor data (RGB images) and learns a NeRF model of the scene. The agents then share their learned NeRF network weights with their neighbors, rather than transferring raw sensor data, to maintain consensus and converge to a unified scene representation.
The key highlights of the approach are:

Efficient communication: By sharing only the learned network weights instead of raw sensor data, the approach significantly reduces the communication overhead, making it suitable for multi-agent systems with limited bandwidth.

Regularization through distribution: Distributing the data across multiple agents serves as an effective regularization technique, reducing NeRF overfitting compared to centralized training, where a single agent has access to the entire dataset. This is particularly beneficial in scenarios with sparse input views.

Comparable performance to centralized training: The experimental results on various datasets demonstrate that the multi-agent approach achieves reconstruction quality comparable to centralized training, where all data is available to a single agent.

Robustness to communication disruptions: The authors investigate the performance of the multi-agent approach under varying communication conditions, showing that the method can still effectively capture the overall scene structure even with reduced communication frequency.

Overall, the proposed distributed learning framework enables collaborative multi-robot perception using NeRFs, achieving efficient communication and improved regularization without sacrificing reconstruction quality compared to centralized training.

Statistiche

The centralized training approach requires transferring 17,390.592 MB of raw sensor data to a central server.
In the multi-agent setup with three agents, each agent receives only 1,764.6492 MB of data (network weights) at 100% communication frequency, or 441.16230 MB at 25% frequency.

Citazioni

"Our distributed learning framework ensures consistency across agents' local NeRF models, enabling convergence to a unified scene representation."
"We show that distributing data across multiple agents and enforcing consensus on the NeRF network weights reduces overfitting in sparse-view scenarios, compared to centralized training."

Approfondimenti chiave tratti da

Distributed NeRF Learning for Collaborative Multi-Robot Perception

by Hongrui Zhao... alle arxiv.org 10-01-2024

https://arxiv.org/pdf/2409.20289.pdf

Domande più approfondite

How can the proposed distributed learning framework be extended to handle dynamic environments where the scene changes over time?

To extend the proposed distributed learning framework for dynamic environments, several strategies can be implemented. First, the framework could incorporate a temporal component into the NeRF model, allowing it to adapt to changes in the scene over time. This could involve maintaining a history of learned representations and employing techniques such as recurrent neural networks (RNNs) or temporal convolutional networks (TCNs) to capture the dynamics of the environment.
Additionally, agents could be equipped with mechanisms to detect changes in the environment, such as using change detection algorithms that identify significant differences in the captured sensory data. When a change is detected, agents could trigger a re-evaluation of the shared model weights, allowing for real-time updates to the NeRF representation.
Moreover, the consensus mechanism could be adapted to prioritize recent data, ensuring that the model remains relevant to the current state of the environment. This could involve weighting the contributions of local data based on recency, thereby allowing the system to dynamically adjust to new information while still benefiting from the collaborative learning aspect of the multi-agent framework.

What are the potential challenges and limitations of the multi-agent approach in terms of scalability and robustness to agent failures or network partitions?

The multi-agent approach presents several challenges and limitations regarding scalability and robustness. One significant challenge is the communication overhead that can arise as the number of agents increases. While the framework reduces the need to transfer raw data, the exchange of model weights among a larger number of agents can still lead to increased bandwidth requirements, especially in scenarios with limited communication resources.
In terms of robustness, the system may be vulnerable to agent failures or network partitions. If an agent becomes unresponsive or loses connectivity, it could disrupt the consensus process, leading to inconsistencies in the learned NeRF models. This could result in degraded performance, particularly if the failed agent was responsible for capturing critical parts of the scene.
To mitigate these issues, the framework could implement fault-tolerant mechanisms, such as allowing agents to continue learning independently for a limited time before attempting to re-establish consensus. Additionally, redundancy could be introduced by having overlapping fields of view among agents, ensuring that critical information is captured even if some agents fail.

Could the distributed learning principles be applied to other types of neural representations beyond NeRFs to enable efficient collaborative perception in multi-robot systems?

Yes, the distributed learning principles outlined in the proposed framework can be applied to various types of neural representations beyond NeRFs, facilitating efficient collaborative perception in multi-robot systems. For instance, similar approaches could be utilized with other implicit representations, such as signed distance functions (SDFs) or occupancy grids, which are commonly used in robotic mapping and navigation tasks.
Moreover, the principles of sharing model weights and maintaining consensus can be adapted for deep learning models used in object detection, semantic segmentation, or even reinforcement learning. In these cases, agents could collaboratively learn to identify and classify objects in their environment, sharing learned features or weights to improve overall performance while minimizing communication costs.
Furthermore, the framework could be extended to incorporate multi-modal sensory data, allowing agents to learn from various inputs such as LiDAR, RGB-D images, and audio signals. This would enhance the robustness and accuracy of the perception system, enabling agents to create a more comprehensive understanding of their environment collaboratively. Overall, the distributed learning principles provide a versatile foundation for advancing collaborative perception across a wide range of neural representation types in multi-robot systems.