VOTE (arXiv)
2025
- arXivJuyi Lin, Amir Taherin, Arash Akbari, and 11 more authors2025
Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language. However, current VLA models suffer from two drawbacks: (i) generation of massive tokens leading to high inference latency and increased training cost, and (ii) insufficient utilization of generated actions resulting in potential performance loss. To address these issues, we develop a training framework to finetune VLA models for generating significantly fewer action tokens with high parallelism, effectively reducing inference latency and training cost. Furthermore, we introduce an inference optimization technique with a novel voting-based ensemble strategy to combine current and previous action predictions, improving the utilization of generated actions and overall performance. Our results demonstrate that we achieve superior performance compared with state-of-the-art VLA models, achieving significantly higher success rates and 39x faster inference than OpenVLA with 46 Hz throughput on edge platforms, demonstrating practical deployability.
@misc{lin2025vote, title = {VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting}, author = {Lin, Juyi and Taherin, Amir and Akbari, Arash and Akbari, Arman and Lu, Lei and Chen, Guangyu and Padir, Taskin and Yang, Xiaomeng and Chen, Weiwei and Li, Yiqian and Lin, Xue and Kaeli, David and Zhao, Pu and Wang, Yanzhi}, year = {2025}, }