W&B Models: Advanced visualization for robotics AI workflows
Learn how some of the most innovative teams in the physical AI space use Weights & Biases for their robotics workflows
Last updated: March 16, 2026
Robotics AI models are fundamentally different from many other AI systems. They are specialized, multimodal, and often embodied, requiring these models to perceive, reason, and act in the physical world. As a result, robotics teams must iterate rapidly across complex model stacks that combine simulation, training, and evaluation. Managing this experimentation process efficiently is essential for making progress.
Evaluating robotics models often requires comparing rich, multimodal outputs such as images, videos, trajectories, and simulation results. Traditionally, this process has been time-consuming and error-prone, with researchers manually reviewing outputs across multiple tools and experiment runs. As workflows grow more complex, it becomes easy to overlook subtle regressions or miss important insights into model behavior.
To make this process faster, W&B Models provides advanced visualization tools designed for robotics and embodied AI workflows. In this post, we’ll walk through five key features that help robotics teams more easily compare experiments, analyze results, and evaluate model performance across multimodal outputs.
Synchronized video playback
Robotics experiments often generate large volumes of video from simulations, sensors, and real world evaluations. Analyzing these outputs across multiple runs can be difficult when videos must be opened and scrubbed through individually. Subtle differences in model behavior such as timing changes, control instability, or perception errors can easily be missed when comparisons are not aligned in time.
Synchronized video playback in W&B Models lets you compare multiple experiment videos side by side in sync. By playing videos simultaneously, researchers can observe how different models behave under the same conditions and quickly identify changes in policy behavior, task execution, or interactions with the environment. This makes it easier for robotics teams to evaluate improvements and detect regressions across experiments.
Pinned runs and baseline comparisons
Evaluating robotics models often requires comparing new policies or architectures against trusted experiments across complex training and simulation outputs. Pinned runs and baseline comparisons make this process faster and more consistent.
You can set a baseline run and pin up to five runs to keep important experiments visible at the top of the run selector, regardless of the filters applied in the workspace. This ensures that your most relevant reference runs, such as a previous policy checkpoint, a stable controller, or a simulation benchmark, are always easy to access as you explore new results. When a baseline is set, it is highlighted directly in line plots, making it easy to compare trends and performance at a glance. This helps robotics teams quickly determine whether a new model improves key metrics such as reward, task success rate, trajectory stability, or training efficiency compared to a known baseline.
Semantic coloring
As robotics experiments scale, comparing large numbers of runs can quickly become difficult. When dozens or hundreds of experiments appear in the same workspace, it can be challenging to visually distinguish which runs correspond to different model configurations, datasets, or training settings.
Semantic coloring in W&B Models automatically groups and color codes runs based on selected parameters or metrics. By assigning consistent colors to runs that share the same configuration values, researchers can immediately see how different settings influence performance across charts and visualizations.
Full image view with zoom and pan
Robotics experiments often generate large volumes of visual outputs such as camera frames, segmentation maps, and perception model predictions. Reviewing these images in detail is critical for understanding how models interpret the environment, but switching between tools or downloading files for closer inspection can slow down the evaluation process.
With the full image view in W&B Models, you can examine images directly within your experiment workspace. The viewer supports zoom and pan, allowing you to closely inspect specific regions of an image without leaving the experiment. This makes it easier to analyze model predictions, spot perception errors, and investigate subtle differences across runs.
Side-by-side image and video comparison
Evaluating multimodal model outputs often requires opening multiple tabs, downloading files, stitching media together, or building custom tools just to compare results. This is common across computer vision, generative media, and robotics workflows where images and videos are critical for understanding model behavior. W&B Models simplifies this process by letting you place images or videos from different runs or checkpoints side-by-side directly in your workspace. You can compare up to four images or four videos in a single view, making it easy to assess outputs without switching tabs or manually assembling comparisons.
The comparison panel also includes a fan out view, which lets you expand a tile to see media across multiple steps or indices at once, helping researchers observe how model outputs evolve during training. Your comparison configuration persists with the workspace, so you can return to the same setup without reconfiguring it. For robotics teams, this makes it much easier to compare evaluation videos across policy checkpoints or camera feeds, confirm that a new checkpoint improves over a baseline, and quickly identify regressions or improvements in model behavior.
Accelerating robotics AI experimentation with W&B Models
W&B Models helps robotics teams streamline experimentation workflows by unifying simulation and training tracking with just a few lines of code. With advanced visualization and comparison tools, robotics AI researchers and engineers can track, compare, and reproduce experiments across multimodal pipelines while maintaining full visibility into model behavior. Watch the demo video below and visit the physical AI webpage to learn more.
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