Two new research papers introduce methods to accelerate the inference speed of Vision-Language-Action (VLA) models used for robot control. KERV utilizes a Kalman Filter to predict actions and adjust acceptance thresholds, achieving up to 37% acceleration with minimal success rate loss. HeiSD proposes a hybrid approach combining different speculative decoding techniques with kinematic awareness, reaching up to 2.45x speedup in simulations and 2.41x in real-world scenarios while maintaining high success rates. AI
影响 New techniques could significantly speed up robot control inference, enabling more responsive and efficient embodied AI systems.
排序理由 Two academic papers published on arXiv introduce novel methods for accelerating VLA models.
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