6 papers
Difference-Aware Retrieval Policies for Imitation Learning
Quinn Pfeifer, Ethan Pronovost, Paarth Shah +3
Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment. We show that reusi…
ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
Yunchu Zhang, Shubham Mittal, Zhengyu Zhang +3
Visuomotor policies often suffer from perceptual challenges, where visual differences between training and evaluation environments degrade policy performance. Policies relying on s…
VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision
Yi Xu, Yuxin Hu, Zaiwei Zhang +5
Human drivers rely on commonsense reasoning to navigate diverse and dynamic real-world scenarios. Existing end-to-end (E2E) autonomous driving (AD) models are typically optimized t…
Generative Data Mining with Longtail-Guided Diffusion
David S. Hayden, Mao Ye, Timur Garipov +6
It is difficult to anticipate the myriad challenges that a predictive model will encounter once deployed. Common practice entails a reactive, cyclical approach: model deployment, d…
Causal Composition Diffusion Model for Closed-loop Traffic Generation
Haohong Lin, Xin Huang, Tung Phan-Minh +6
Simulation is critical for safety evaluation in autonomous driving, particularly in capturing complex interactive behaviors. However, generating realistic and controllable traffic…
DriveGPT: Scaling Autoregressive Behavior Models for Driving
Xin Huang, Eric M. Wolff, Paul Vernaza +13
We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent…