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Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control
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Deep neural network (DNN)-based policy models like vision-language-action (VLA) models are transformative in automating complex decision-making across applications by interpreting multi-modal data. However, scaling these models greatly increases computational costs, which presents challenges in fields like robot manipulation and autonomous driving that require quick, accurate responses. To address the need for deployment on resource-limited hardware, we propose a new quantization framework for IL-based policy models that fine-tunes parameters to enhance robustness against low-bit precision errors during training, thereby maintaining efficiency and reliability under constrained conditions. Our evaluations with representative robot manipulation for 4-bit weight-quantization on a real edge GPU demonstrate that our framework achieves up to 2.5x speedup and 2.5x energy savings while preserving accuracy. For 4-bit weight and activation quantized self-driving models, the framework achieves up to 3.7x speedup and 3.1x energy saving on a low-end GPU. These results highlight the practical potential of deploying IL-based policy models on resource-constrained devices.
Forward citations
Cited by 6 Pith papers
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Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference
Token pruning for VLA models should use both prefill semantic attention and temporally smoothed action-decode attention; VLA-Pruner does this and preserves manipulation performance at high prune ratios.
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SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models
A training-free framework jointly quantizes a VLA model to 4 bits and prunes visual tokens, recovering or exceeding full-precision success rates at 1.93x speedup.
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Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach
The claimed result is that source-component-shift adaptation splits cleanly into offline component learning via EM and online mixing-weight updates, cutting cumulative test loss by up to 67.4%.
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Leveraging OS-Level Primitives for Robotic Action Management
Applying OS-style exception handling, context caching, and replay to robotic action slices raises success rates 7x to 24x and cuts execution steps up to 74% for repetitive manipulation tasks, without retraining the VLA model.
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Think Twice, Act Once: Token-Aware Compression and Action Reuse for Efficient Inference in Vision-Language-Action Models
FlashVLA, a training-free plug-in, reuses stable actions and prunes visual tokens to cut VLA model inference FLOPs by 55.7% and latency by 36% with only a 0.7% success-rate drop on LIBERO.
- RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models
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