SpikingVLA: Asynchronous Spiking Vision-Language-Action Models

Vision-Language-Action (VLA) models have become a dominant paradigm for embodied intelligence. However, most existing approaches are built on large-scale transformers, resulting in substantial inference latency and energy consumption that limit their practical deployment in low-power, real-time scenarios. We propose SpikeVLA, a spiking VLA architecture for embodied navigation with energy-efficient inference, consisting of three key components. (i) A spiking vision encoder, Spike-V, that replaces dense continuous layers with event-driven spiking layers to reduce the energy consumption of visual representation learning. (ii) A multi-modal spiking large language model, Spike-L, that reformulates cross-modal reasoning with spiking dynamics and token-level event-driven sparsity to further lower computational cost. (iii) A spiking action policy network, Spike-A employs Laplacian-kernel population coding with a multi-layer fully connected SNN, and decodes spiking activities into stable and robust continuous control with energy-efficient inference under low-power constraints. Experiments on navigation and robotic control tasks show that SpikeVLA significantly reduces energy consumption and computational cost while maintaining competitive performance, highlighting its potential for low-power, real-time embodied intelligence.

Spiking neural networks (SNNs) are brain-inspired, event-driven models that compute with sparse spikes, which enables highly efficient visual perception in resource-constrained embodied AI models. The emergence of Spiking-Transformer models with spike self-attention has substantially improved the learning capacity of pure SNNs. Although SNNs are energy efficient, their performance is still limited by the spike-based architecture and optimization challenges, as standard gradient descent rules cannot be directly applied. Recently, vision-language models (VLMs) have shown rich multi-modal knowledge representation capabilities for visual perception. Thus, it is promising to leverage VLMs for better Spikformer training. To this end, we present VL2Spike, a novel spike-based knowledge distillation (KD) framework that bridges multi-modal knowledge from VLMs with compact Spikformer models. This design enhances the learning capacity of Spikformer models while preserving their energy-efficiency merits, thereby offering a practical pathway toward low-power robotic perception. Our VL2Spike brings two key technical contributions. To align with spiking dynamics, we first propose spatial-temporal visual spike (SVS) distillation, which achieves (1) shared manifold alignment between VLM image features and spike tokens, and (2) warm-started temporal consistency on membrane potentials and spike rates. We then design a novel spike prototype-guided linguistic (SPL) distillation strategy that aligns Spikformer's class prototypes and logits with promptable VLM text embeddings. Extensive experiments show that VL2Spike achieves 6.81% gain across three static datasets with only 15.7% energy consumption. It also exhibits strong generalization capacity on robotic visual place recognition (VPR) with a gain of 6.63%, highlighting its potential for low-power perception in embodied AI.

Vision-Language-Action (VLA) models offer a promising path to generalist robot control, but their inference latency causes observation staleness when generated actions are executed asynchronously. Several methods have been proposed concurrently to mitigate this problem: inference-time inpainting (IT-RTC), training-time delay simulation (TT-RTC), future-state-aware conditioning (VLASH), and lightweight residual correction (A2C2). Each takes a fundamentally different approach, but they have so far been evaluated independently with different codebases, base policies, and protocols. We present a systematic comparison of these four methods under controlled conditions. We develop two unified codebases that integrate all methods with harmonized library and dataset versions, and we benchmark them on the Kinetix suite with MLPMixer policies and on the LIBERO manipulation benchmark with SmolVLA, sweeping inference delays up to $d=20$ control steps. A2C2's per-step residual correction is the most effective method on Kinetix, holding above 90% solve rate up to $d=8$, and also leads on LIBERO from $d=4$ onwards. IT-RTC is competitive at low delays but degrades sharply under long chunks ($H=30$) and high delays. TT-RTC is the most robust training-based method: stable across d_\max choices, generalizes beyond its training delay distribution, and adds zero inference overhead. VLASH exhibits a clear low-delay vs. high-delay trade-off governed by the fine-tuning delay range [0,d_\max]. Code is available at https://github.com/TheAyos/async-vla-inference