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Scientists Develop Method to Steer Robot Behavior with Interpretable AI

Berkeley, CA - Researchers at the University of California, Berkeley, have developed a novel method to interpret and control the behavior of advanced robotics models. These Vision-Language-Action (VLA) models, which are trained to understand visual scenes and language commands to perform physical tasks, are becoming increasingly sophisticated. However, their complex inner workings have made them difficult to understand and reliably control, a significant hurdle for their deployment in real-world applications where safety and predictability are paramount.

The new framework, detailed in a recent paper, draws inspiration from advances in “mechanistic interpretability” for large language models (LLMs). It allows researchers to peer into the internal representations of VLA models and directly influence their actions at inference time, without needing to retrain the model or provide additional environmental feedback.

Unpacking the “Black Box” of Robot AI

At its core, the research focuses on analyzing the “value vectors” within the feed-forward network (FFN) layers of transformer models, which are a common component in VLAs. These vectors are essentially internal representations that the model uses to process information. The researchers discovered that a significant portion of these vectors, even in models trained solely for action prediction, retain interpretable semantic meanings related to concepts like “fast,” “slow,” “up,” or “down.”

“We were surprised to find that even though the models are trained to produce only action commands, their internal workings are still rich with semantic information,” explained Bear Häon, lead author of the study. “It’s like finding hidden language within the robot’s brain.”

From Concepts to Control: Steering Robot Actions

The key breakthrough is the ability to leverage these identified semantic concepts to steer the robot’s behavior. By identifying specific neuron clusters in the FFN that correspond to these semantic directions, the researchers can then amplify or suppress their activations during the model’s decision-making process.

For instance, in simulations, they were able to control the speed and trajectory of a robot arm. By activating “slow” semantic directions, the robot consistently moved more deliberately, resulting in a lower maximum height when picking up and placing a toy. Conversely, activating “fast” directions led to quicker movements.

This concept was further demonstrated on a physical UR5 robot arm. The researchers successfully steered the robot to perform pick-and-place tasks with variations in height and speed. When the “low” concept was activated, the robot arm moved the object at a lower height. Similarly, the “slow” intervention resulted in noticeably slower movements. Crucially, these interventions were effective even when the robot’s direct instructions, or prompts, were kept the same, highlighting the internal steering capabilities.

A New Paradigm for Robotics Control

This work introduces a promising new paradigm for controlling embodied AI systems. Instead of relying solely on prompt engineering or extensive retraining, this method offers a more transparent and direct way to influence robot behavior. The researchers emphasize that this approach is “zero-shot,” meaning it works without additional training data or interaction with the environment for each new intervention.

“This is a significant step towards building more transparent and steerable foundation models for robotics,” stated Professor Claire Tomlin, a senior author on the paper. “By understanding the internal semantic components, we can create AI systems that are not only capable but also more predictable and controllable.”

The findings suggest that VLA models internalize semantic concepts during pre-training in a way that supports compositional reasoning across different layers. This research opens doors for further exploration into the interpretability and controllability of complex AI systems in robotics, paving the way for safer and more reliable embodied agents.