10 papers
SkiP: When to Skip and When to Refine for Efficient Robot Manipulation
Mingtong Dai, Guanqi Peng, Yongjie Bai +5
Previous imitation learning policies predict future actions at every control step, whether in smooth motion phases or precise, contact-rich operation phases. This uniform treatment…
CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies
Fan Du, Feng Yan, Jianxiong Wu +8
Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi-step inference is required to re…
RoboTron-Mani: All-in-One Multimodal Large Model for Robotic Manipulation
Feng Yan, Fanfan Liu, Liming Zheng +5
Recently, robotics has advanced significantly through the integration of larger models and large-scale datasets. However, challenges remain in applying these models to 3D spatial i…
RoboTron-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction
Yufeng Zhong, Chengjian Feng, Feng Yan +3
In language-guided visual navigation, agents locate target objects in unseen environments using natural language instructions. For reliable navigation in unfamiliar scenes, agents…
RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving
Zhijian Huang, Chengjian Feng, Feng Yan +5
Large Multimodal Models (LMMs) have demonstrated exceptional comprehension and interpretation capabilities in Autonomous Driving (AD) by incorporating large language models. Despit…
RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case
Baihui Xiao, Chengjian Feng, Zhijian Huang +3
Collecting real-world data for rare high-risk scenarios, long-tailed driving events, and complex interactions remains challenging, leading to poor performance of existing autonomou…