Log Standards and Visualization Tools for Self-Organizing Behaviors in Embodied AI Robots

Self‑Organizing Behavior Log Standards

MCAP (Message Capture)

Heterogeneous data compatibility : stores any serialized format (ROS messages, Protobuf, FlatBuffers, JSON, images, point clouds) in a single file, enabling joint, visual, force, and environment data to be recorded together.

High‑performance read/write : append‑only storage with built‑in indexing provides real‑time high‑throughput recording and fast random access without scanning the whole file.

Self‑contained reliability : embeds all message schemas, includes CRC checks, and supports resume after interruptions, ensuring integrity for long‑duration recordings.

Cross‑platform SDKs : C++, Python, Go, TypeScript libraries run on Linux, Windows, macOS and embedded systems; compatible with ROS 1, ROS 2 and custom frameworks.

In a humanoid‑robot test, MCAP recorded joint angles at 100 Hz, RGB‑D images at 30 Hz, force‑sensor data at 500 Hz, and point clouds at 10 Hz in a single file that held several hours of continuous behavior. Indexed access allowed rapid pinpointing of anomalous intervals for visual analysis.

ROS Bag 2.0

ROS 2’s upgraded log format replaces ROS Bag 1.0 with a modular design supporting multiple storage back‑ends. The default back‑end is SQLite; a plugin can switch to MCAP. Advantages include seamless ROS 2 integration and direct recording of all ROS 2 message types without adapters. Compared with MCAP, the SQLite back‑end provides lower storage efficiency, weaker high‑throughput write performance, and limited cross‑platform support, so ROS Bag 2.0 is now mainly used for lightweight ROS 2‑centric scenarios and is being superseded by MCAP.

ULG (UAV Log Format)

Originally designed for UAVs, ULG is a binary format that records only essential state data (position, velocity, attitude, basic command signals). It does not support large payloads such as images or point clouds, resulting in minimal storage overhead and fast parsing. ULG is therefore suited for resource‑constrained platforms—e.g., small service or educational robots that log basic navigation and obstacle‑avoidance behaviors.

Self‑Organizing Behavior Visualization Tools

PlotJuggler

Time‑series visualization : plots joint angles, velocities, sensor values, and command signals as line, bar, or scatter charts, allowing simultaneous comparison of multiple dimensions.

Multimodal data linking : synchronously replays RGB images, depth maps, and point clouds alongside time‑series charts, enabling rapid identification of whether an anomaly originates from perception, environment, or decision logic.

3‑D model replay : imports URDF robot models and animates them using logged joint and pose data, providing viewpoint‑independent inspection of trajectories, end‑effector poses, and collision risks.

Data export and annotation : exports visualizations to CSV, PNG, GIF and supports event tagging for collaborative analysis.

In an engineering case, PlotJuggler was used to diagnose a mechanical‑arm adaptive‑grasp failure. By aligning visual feeds with joint‑angle plots and a 3‑D replay, the team discovered that a sensor occlusion caused target‑position errors, which produced abnormal joint trajectories.

Roboto

Multi‑format compatibility and batch processing : accepts MCAP, ROS Bag, ULG, images, videos, JSON, etc.; supports bulk upload, storage, and tag‑based retrieval (by task, platform, test time).

Advanced visualization and analysis : provides time‑series charts, 3‑D replay, multi‑view video sync, point‑cloud rendering, and built‑in anomaly‑detection algorithms that automatically flag sensor noise, motion spikes, or task failures and generate reports.

Team collaboration and data sharing : online viewing, annotation, commenting, and a Python SDK/CLI for CI/CD integration, enabling automated log upload, analysis, and report generation.

robot‑log‑visualizer

A lightweight Python tool targeting YARP‑based robots. It reads .mat log files and offers basic time‑series plots, joint‑state visualization, and simple 3‑D model replay. Its minimal resource consumption makes it suitable for small research projects and lightweight platforms.

WandB (Weights & Biases)

Through the robot-eval-logger plugin, WandB can ingest robot logs and visualize time‑series data, video playback, success‑rate statistics, and model‑performance comparisons. Cloud storage and team‑collaboration features allow coupling of self‑organizing behavior logs with reinforcement‑learning training loops, enabling real‑time monitoring of robot behavior and reward curves for rapid hyper‑parameter tuning.