8 papers
SysNav: Multi-Level Systematic Cooperation Enables Real-World, Cross-Embodiment Object Navigation
Haokun Zhu, Zongtai Li, Zihan Liu +8
Object navigation (ObjectNav) in real-world environments is a complex problem that requires simultaneously addressing multiple challenges, including complex spatial structure, long…
Interacting safely with cyclists using Hamilton-Jacobi reachability and reinforcement learning
Aarati Andrea Noronha, Jean Oh
In this paper, we present a framework for enabling autonomous vehicles to interact with cyclists in a manner that balances safety and optimality. The approach integrates Hamilton-J…
LongComp: Long-Tail Compositional Zero-Shot Generalization for Robust Trajectory Prediction
Benjamin Stoler, Jonathan Francis, Jean Oh
Methods for trajectory prediction in Autonomous Driving must contend with rare, safety-critical scenarios that make reliance on real-world data collection alone infeasible. To asse…
MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments
Zhixuan Liu, Haokun Zhu, Rui Chen +4
We introduce a diffusion-based approach for generating privacy-preserving digital twins of multi-room indoor environments from depth images only. Central to our approach is a novel…
STRIVE: Structured Representation Integrating VLM Reasoning for Efficient Object Navigation
Haokun Zhu, Zongtai Li, Zhixuan Liu +4
Vision-Language Models (VLMs) have been increasingly integrated into object navigation tasks for their rich prior knowledge and strong reasoning abilities. However, applying VLMs t…
RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding
Benjamin Stoler, Juliet Yang, Jonathan Francis +1
Safety-critical scenarios are essential for training and evaluating autonomous driving (AD) systems, yet remain extremely rare in real-world driving datasets. To address this, we p…