From the 2 of 9 linked papers with an AI index.
9 papers
A Glimpse into Long-term Physical Coexistence with Intelligent Robots
Weiqi Jin, Peijun Tang, Kuncheng Luo +5
The paper presents PHILIA, a modular multi‑robot system that separates high‑level reasoning from low‑level robot execution via a robot‑gateway interface, enabling long‑term, person…
Towards Predictive, Aligned, and Scalable Robot Learning
Peijun Tang, Shangjin Xie, Baifu Huang +6
The paper introduces Lumo-2, a latent world-action model that reasons about future physical dynamics in a shared latent space to generate robot actions, using a multi‑stage alignme…
Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving
Xuanjin Jin, Yanxin Dong, Bin Sun +4
Planning under uncertainty for real-world robotics tasks, such as autonomous driving, requires reasoning in enormous high-dimensional belief spaces, rendering the problem computati…
Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
Qiqi Liu, Huan Xu, Jingyu Li +5
Autonomous driving requires reasoning about how the environment evolves and planning actions accordingly. Existing world-model-based approaches typically predict future scenes firs…
FLARE: Learning Future-Aware Latent Representations from Vision-Language Models for Autonomous Driving
Chengen Xie, Chonghao Sima, Tianyu Li +4
While Vision-Language Models (VLMs) offer rich world knowledge for end-to-end autonomous driving, current approaches heavily rely on labor-intensive language annotations (e.g., VQA…
From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Driving
Sining Ang, Yuguang Yang, Chenxu Dang +8
Vision-Language-Action (VLA) driving augments end-to-end (E2E) planning with language-enabled visual backbones, yet it remains unclear how vision-language models (VLMs) differ from…