Actionable Interpretability for Robotics AI

Sculpting
Embodied Intelligence

Interpret, debug, and steer Vision-Language-Action models, unlocking real world deployment.

Platform Features

Interpretability that Works

Open-source suite for deep VLA interpretability. Leverage attention heatmaps, sparse autoencoders, and inference breakpoints.

Real-time Attention Maps

Visualize exactly what your model is looking at, frame by frame. Track attention patterns across visual, language, and action modalities in real-time.

Feature Circuit Analysis

Trace the flow of information through your model. Identify which neurons activate for specific behaviors and understand the computational graph.

Behavior Intervention

Directly modify model outputs in real-time. Correct undesirable actions, run counterfactual simulations, and trace issues back to training data.

Core Workflow

01

capture

Record comprehensive telemetry and internal model states from your VLA models.

02

analyze

Decode reasoning patterns and correlate neural activity with robot behaviors.

03

intervene

Modify behaviors and run experiments to understand model decision-making.

Open-SourcePyTorch NativeReal-time Inference
blog

blog

Research updates and technical insights from the Parametric team, focusing on VLA interpretability and the future of explainable robotic AI.

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Interpretability Is Useful: Case Studies

Parametric Research Team

May 12, 2025

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Training and deploying a VLA?

We can help you understand, debug, and optimize your Vision-Language-Action models. Join our waitlist to get early access to our interpretability platform designed specifically for robotics AI.