rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference

Problem

The paper addresses a gap in existing vision-language-action (VLA) inference frameworks, which do not effectively exploit embodied workloads and distinct bottlenecks. This limitation hinders the efficiency of VLA models in real-time applications, particularly in robotics and interactive systems. The work is presented as a preprint and has not undergone peer review.

Method

The authors propose rMuscle, a real-time VLA inference framework designed to optimize computational efficiency. The core of rMuscle is a dual-phase muscle-memory cache mechanism that consists of two components:

• Context Cache: This component reuses visual-token outputs from previous computations to minimize redundant calculations, thereby reducing overall computational load.
• Action Cache: This component leverages neuron activation patterns from prior inferences to decrease the frequency of weight accesses, further enhancing efficiency.

Additionally, rMuscle employs several techniques to improve cache performance:

• Online Cache Recomputation: This allows for dynamic updates to the cache based on real-time data, ensuring that the most relevant information is readily available.
• Sliding-Window Cache Retrieval: This technique facilitates efficient access to cached data by maintaining a window of recent computations, optimizing retrieval times.
• Mask Sharing Across Consecutive Denoising Steps: This approach reduces the computational burden during the denoising process by sharing masks, which helps streamline the inference pipeline.

Results

The implementation of rMuscle demonstrates a speedup of 1.29 to 1.42 times compared to the original VLA inference methods. This improvement is validated across multiple benchmarks, including LIBERO, RoboTwin, and physical manipulation tasks, using hardware platforms such as the RTX 4090 and Jetson Thor.

Limitations

The authors do not report any limitations in their work, and the available text does not indicate any obvious shortcomings in the proposed framework.

Why it matters

The introduction of rMuscle has significant implications for the efficiency of VLA models in real-time applications, particularly in robotics and interactive environments. By addressing the computational bottlenecks inherent in VLA inference, this framework paves the way for more responsive and capable systems that can better integrate vision, language, and action. The techniques developed in this work may also inspire further research into caching mechanisms and optimization strategies in other areas of machine learning.