JuiceFS for Robotics

Why JuiceFS?

Unified storage across the entire pipeline

Robot data pipelines span multiple stages: collection, replay, data processing, training, and simulation. JuiceFS supports all these workloads within a single storage system, reducing data movement and eliminating silos.

Accelerated access to hot data

Robot workloads are highly sensitive to hot data access latency. JuiceFS provides local caching, distributed caching, read‑ahead, and warm-up capabilities to improve replay and training efficiency and reduce repeated access to object storage.

Simplified multi‑cloud and cross‑region collaboration

When using GPU resources across multiple regions, JuiceFS’ mirror file system feature ensures near‑local access and consistency for data worldwide. It effectively reduces the cost burden of cross‑region access and optimizes data scheduling and distribution.

Lightweight dataset version management

Robot algorithm iteration relies on large volumes of historical data for reproduction, simulation, and result comparison. Physically copying datasets for version management leads to high storage and operational costs. JuiceFS offers lightweight, metadata-based cloning and version management, enabling rapid dataset replication to support experiment branching, result comparison, rollback, and parallel development.

Efficient management of massive small files

Robot platforms continuously generate massive files, including images, point clouds, logs, maps, and labeling results. JuiceFS optimizes directory traversal, metadata querying, and small-file access through a high-performance metadata engine and caching mechanisms, making it suited for managing massive small files in robotics scenarios.

Cloud-native design

Robot platforms are increasingly running on Kubernetes, supporting various containerized workloads such as training jobs, data pipelines, labeling services, and simulation tasks. JuiceFS is built for cloud environments. It can be deployed across global public clouds and seamlessly integrated into existing cloud infrastructures, adapting to different cloud platforms and regional requirements.

Feature Overview

Distributed cache

Multiple clients share the same cache data to enhance performance.

In-house metadata

JuiceFS' metadata engine is horizontally scalable. It efficiently manages storage for hundreds of billions of files within a single namespace.

Mirror file systems

Creating one or more complete mirrors of the file system with consistent content.

Superior performance

Check fio performance test results, including sequential and concurrent reads/writes for both large and small files.

POSIX compatibility

You can use it like a local file system, seamlessly integrating with existing applications without disrupting application operations.

JuiceFS CSI Driver

Implementing the interface between container orchestration systems and JuiceFS. In K8s, JuiceFS can provide persistent volumes for Pods.

D-Robotics Manages Massive Small Files in a Multi-Cloud Environment with JuiceFS

D-Robotics specializes in the research and development of foundational computing platforms for consumer-grade robots. In 2025, the company released an embodied AI foundation model. JuiceFS meets its storage performance requirements through efficient cross-cloud adaptation and small-file caching mechanisms, delivering high cost-effectiveness by balancing cost and efficiency.

coScene Chose JuiceFS over Alluxio to Tackle Object Storage Drawbacks

coScene specializes in post-deployment robot operations, providing closed-loop data services including data collection, storage, visualization, and simulation training. By adopting JuiceFS, the company has solved the challenges of massive small-file management and multi-cloud data storage in robotics scenarios, improving data processing efficiency and simplifying operations.