Many robotic manipulation tasks require sensing and responding to force signals such as torque to assess whether the task has been successfully completed and to enable closed-loop control. However, current Vision-Language-Action (VLA) models lack the ability to integrate such subtle physical feedback. In this work, we explore Torque-aware VLA models, aiming to bridge this gap by systematically studying the design space for incorporating torque signals into existing VLA architectures. We identify and evaluate several strategies, leading to three key findings. First, introducing torque adapters into the decoder consistently outperforms inserting them into the encoder. This is because torque signals align more closely with the decoder’s input, and the decoder is more sensitive to variations in input. Second, torque history proves to be a critical signal. We find that the most effective way to incorporate it is by summarizing the entire history into a single token, as this preserves the original input pattern of the decoder. Third, inspired by joint prediction and planning paradigms in autonomous driving, we propose predicting torque as an auxiliary output, which further improves performance. This strategy encourages the model to build a physically grounded internal representation of interaction dynamics. Extensive quantitative and qualitative experiments across contact-rich manipulation benchmarks validate our findings. Code, models, and datasets will be released.

In this section, we present a video demonstrating torque variations in real-world scenarios. The video showcases a robot arm performing tasks with varying joint torques. From left to right, we present three tasks: Charger Plugging, USB Plugging, and Button Pushing. For each task, the first video provides a top-down view, the second video offers a front view, and the third video visualizes the torque variations. These three videos are time-synchronized and played at 1x speed.