Showing cs.ROShow all
3 papers · 1 filter
cs.RO2026
Recovering Low-Bit Failures in Vision-Language-Action Policies via Quantization-Conditioned Reinforcement Learning
Seongmin Park, Jaewon Lee, Yujun Heo +4
Quantization lowers deployment costs of vision-language-action (VLA) policies but can compromise task completion. Across five VLA models and 4,050 real-world trials, we systematica…
cs.RO2025
Saliency-Aware Quantized Imitation Learning for Efficient Robotic Control
Seongmin Park, Hyungmin Kim, Sangwoo Kim +5
Deep neural network (DNN)-based policy models, such as vision-language-action (VLA) models, excel at automating complex decision-making from multi-modal inputs. However, scaling th…
cs.RO2024
Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control
Seongmin Park, Hyungmin Kim, Wonseok Jeon +4
Deep neural network (DNN)-based policy models like vision-language-action (VLA) models are transformative in automating complex decision-making across applications by interpreting…