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
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IMPACT New techniques could significantly speed up robot control inference, enabling more responsive and efficient embodied AI systems.
RANK_REASON Two academic papers published on arXiv introduce novel methods for accelerating VLA models.