DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for
  Efficient Robot Execution
Authors: name: Yang Yue, name: Yulin Wang, name: Bingyi Kang, name: Yizeng Han, name: Shenzhi Wang, name: Shiji Song, name: Jiashi Feng, name: Gao Huang

MLLMs have demonstrated remarkable comprehension and reasoning capabilities
with complex language and visual data. These advances have spurred the vision
of establishing a generalist robotic MLLM proficient in understanding complex
human instructions and accomplishing various embodied tasks. However,
developing MLLMs for real-world robots is challenging due to the typically
limited computation and memory capacities available on robotic platforms. In
contrast, the inference of MLLMs involves storing billions of parameters and
performing tremendous computation, imposing significant hardware demands. In
our paper, we propose a Dynamic Early-Exit Framework for Robotic
Vision-Language-Action Model (DeeR-VLA, or simply DeeR) that automatically
adjusts the size of the activated MLLM based on each situation at hand. The
approach leverages a multi-exit architecture in MLLMs, which allows the model
to terminate processing once a proper size of the model has been activated for
a specific situation, thus avoiding further redundant computation.
Additionally, we develop novel algorithms that establish early-termination
criteria for DeeR, conditioned on predefined demands such as average
computational cost (i.e., power consumption), as well as peak computational
consumption (i.e., latency) and GPU memory usage. These enhancements ensure
that DeeR operates efficiently under varying resource constraints while
maintaining competitive performance. On the CALVIN robot manipulation
benchmark, DeeR demonstrates significant reductions in computational costs of
LLM by 5.2-6.5x and GPU memory of LLM by 2-6x without compromising performance.
Code and checkpoints are available at https://github.com/yueyang130/DeeR-VLA.