Vision-Language-Action (VLA) models provide a unified framework for perception, language conditioning, and action generation, but many existing systems remain difficult to deploy in embedded robotic settings because of their computational requirements and inference latency. In this paper, we present LiteVLA-Edge, a deployment-oriented VLA pipeline for fully on-device inference on Jetson Orin-class hardware. Our approach combines supervised image-to-action fine-tuning in FP32 with post-training 4-bit GGUF quantization and GPU-accelerated inference through the \texttt{llama.cpp} runtime. Under our deployment configuration, LiteVLA-Edge achieves a mean end-to-end latency of 150.5\,ms (approximately 6.6\,Hz) while operating entirely offline within a ROS~2-integrated perception--reasoning--action pipeline. Rather than introducing a new policy objective, our contribution is a practical systems path for executing compact multimodal control models locally on embedded hardware while preserving modular interfaces between perception, reasoning, and actuation. These results establish timing feasibility for reactive language-conditioned control and provide a reproducible baseline for future task-level evaluation of on-device VLAs in robotics.
Indian Institute of Information Technology, Allahabad（印度理工学院安拉阿巴德分校）
Our way of grasping objects is challenging for efficient, intelligent and optimal grasp by COBOTs. To streamline the process, here we use deep learning techniques to help robots learn to generate and execute appropriate grasps quickly. We developed a Generative Inception Neural Network (GI-NNet) model, capable of generating antipodal robotic grasps on seen as well as unseen objects. It is trained on Cornell Grasping Dataset (CGD) and attained 98.87% grasp pose accuracy for detecting both regular and irregular shaped objects from RGB-Depth (RGB-D) images while requiring only one third of the ne…
IoT devices suffer from resource limitations, such as processor, RAM, and disc storage. These limitations become more evident when handling demanding applications, such as deep learning, well-known for their heavy computational requirements. A case in point is robot pose estimation, an application that predicts the critical points of the desired image object. One way to mitigate processing and storage problems is compressing that deep learning application. This paper proposes a new CNN for the pose estimation while applying the compression techniques of pruning and quantization to reduce his d…
ARP: Enhancing Quantized Skill Abstractions via Visual Alignment and Iterative Refinement for Robotic Manipulation
School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；Key Laboratory of General Artificial Intelligence and Large Models in Provincial Universities, Soochow University（苏州大学省高校通用人工智能与大模型重点实验室）；College of Mechanical and Electronic Engineering, China Jiliang University（中国计量大学机电工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Leju Robotics（乐聚机器人）
E0: Enhancing Generalization and Fine-Grained Control in VLA Models via Tweedie Discrete Diffusion
Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis（广东大数据分析与处理重点实验室）；X-Era AI Lab（X-Era人工智能实验室）；Guangdong University of Technology（广东工业大学）
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos
Joy Future Academy, JD（京东探索研究院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；Tsinghua SIGS, Tsinghua University（清华大学深圳国际研究生院）；Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学院）
Northeastern University（东北大学）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Foshan Graduate School, Northeastern University（东北大学佛山研究生院）
Hermite Curves as Trajectory Priors for Vision-Language-Action Models
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；Jiangsu Cytoderm Intelligent Technology Co., Ltd.（江苏赛克德智能科技有限公司）；National University of Singapore（新加坡国立大学）
