6 citations · 9 across the 7 of their papers we have counts for
7 papers
DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions
Weicheng Zheng, Yixin Huang, Qiao Sun +2
Driving Vision-Language-Action Models (Driving VLAs) commonly introduce natural-language reasoning as an intermediate interface for end-to-end planning, but reasoning-centric inter…
DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions
Weicheng Zheng, Yixin Huang, Qiao Sun +2
Driving Vision-Language-Action Models (Driving VLAs) aim to use language to improve end-to-end planning, but the language-action gap limits this promise. We propose DriveMA, a Driv…
OccVLA: Vision-Language-Action Model with Implicit 3D Occupancy Supervision
Ruixun Liu, Lingyu Kong, Derun Li +1
Multimodal large language models (MLLMs) have shown strong vision-language reasoning abilities but still lack robust 3D spatial understanding, which is critical for autonomous driv…
Learning Personalized Driving Styles via Reinforcement Learning from Human Feedback
Derun Li, Changye Li, Yue Wang +9
Generating human-like and adaptive trajectories is essential for autonomous driving in dynamic environments. While generative models have shown promise in synthesizing feasible tra…
VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and Locomotion
Shaoting Zhu, Linzhan Mou, Derun Li +3
Recent success in legged robot locomotion is attributed to the integration of reinforcement learning and physical simulators. However, these policies often encounter challenges whe…
SARO: Space-Aware Robot System for Terrain Crossing via Vision-Language Model
Shaoting Zhu, Derun Li, Linzhan Mou +3
The application of vision-language models (VLMs) has achieved impressive success in various robotics tasks. However, there are few explorations for these foundation models used in…
Large Trajectory Models are Scalable Motion Predictors and Planners
Qiao Sun, Shiduo Zhang, Danjiao Ma +7
Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding div…