5 papers
SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations
Wei-Jer Chang, Zhouchonghao Wu, Akshay Rangesh +3
Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints, and behaviors differ fundament…
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
Zhouchonghao Wu, Akshay Rangesh, Weixin Li +5
Training robust autonomous driving agents requires a simulator fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and…
TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
Zikang Xiong, Weixin Li, Zhouchonghao Wu +6
End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments. Its standard training recipe, however, is expensive across all sta…
FutureVLA: Joint Visuomotor Prediction for Vision-Language-Action Model
Xiaoxu Xu, Hao Li, Jinhui Ye +7
Predictive foresight is important to intelligent embodied agents. Since the motor execution of a robot is intrinsically constrained by its visual perception of environmental geomet…
RMAdapter: Reconstruction-based Multi-Modal Adapter for Vision-Language Models
Xiang Lin, Weixin Li, Shu Guo +2
Pre-trained Vision-Language Models (VLMs), \textit{e.g.} CLIP, have become essential tools in multimodal transfer learning. However, fine-tuning VLMs in few-shot scenarios poses si…