Researchers improved robot control with the LatentQuant compression method
A new technique allows Wan2.1 and Wan2.2 models to run faster on NVIDIA B300 chips without losing their ability to guide robots.
Researchers recently developed LatentQuant to fix a major problem in video models used for robotics. Many modern systems rely on video encoders to understand the world, but shrinking these models to save space often breaks the link between the AI's vision and its physical actions.
When developers use standard compression on a model like Wan2.1, the video quality might look fine. However, the internal language the model uses to tell a robot how to move becomes garbled. This leads to failures in real-world tasks.
LatentQuant preserves near-baseline control and high reconstruction quality.
LatentQuant solves this by using a two-stage training process. First, it ensures the compressed encoder stays in sync with the original, high-precision version. Then, it freezes that encoder and fine-tunes the decoder to match the new output. This keeps the robot's logic intact while reducing the memory footprint.
Testing showed that this method works well. On the LIBERO benchmark, robots achieved a 95.75% success rate, which is a massive improvement over standard compression techniques. It also provides a significant speedup of up to 1.26x on NVIDIA B300 hardware compared to older formats.
Imagine you are teaching a robot to stack blocks by watching a video. If the video is slightly compressed, the robot might reach for the wrong spot. Does your computer have enough power to run complex models locally if they could be this efficient?
It is important to note that these tests focused on specific benchmarks like LIBERO and RoboTwin. The researchers have not yet tested how LatentQuant performs across a wider variety of diverse, unstructured real-world robotics tasks.
Source: arXiv cs.AI