3 papers indexed
Mengshi Qi, Xiaoyang Bi, Xianlin Zhang, Huadong Ma
Self-supervised depth estimation is challenging for safe autonomous driving under various adverse weather conditions due to sensor perception degradation. These challenges arise from two main aspects. Firstly, adverse conditions can distort pixel correspondences and violate the a…
Zhe Zhao, Zhibin Li, Yilin Ou, Mengshi Qi
Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities but remain difficult to train the control policies that can deploy on real hardware due to contact-rich physics and imperfect actuation. We present a sim-to-real reinforcement learning me…
Zhaohong Liu, Hao Ye, Xianlin Zhang, Mengshi Qi
End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised Fine-Tuning (SFT) often suffers from reasoning hallucinations and conservative biases. While traditional tool-augmented frameworks and Chain-of-Thought (CoT) approa…