12 papers
What Can One Sentence Fix? A Failure Taxonomy and a Minimal Language Correction for Frozen VLA Policies
Owen Kwon, Pablo Ortega-Kral, Arthur Bucker +1
When a deployed vision-language-action (VLA) policy fails, fine-tuning it again for every new setting is costly, yet the person watching it can usually say what went wrong in one s…
ControlledShifts: Towards Standardizing Robustness Evaluation in Trajectory Prediction Under Distribution Shifts
Ingrid Navarro, Pablo Ortega-Kral, Yutong Duan +2
Trajectory prediction is central to safety in autonomous driving, yet learning-based predictors tend to degrade sharply when encountering scenarios poorly represented by their trai…
ScenarioCharacterization: A Modular Toolkit for Characterizing Safety across Trajectory Datasets
Ingrid Navarro, Yutong Duan, Jonathan Francis +1
We introduce ScenarioCharacterization, an open-source framework for automated, dataset-agnostic profiling of driving scenarios in trajectory datasets. Our framework is packaged as…
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…
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…
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…
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…
GRAPPA: Generalizing and Adapting Robot Policies via Online Agentic Guidance
Arthur Bucker, Pablo Ortega-Kral, Jonathan Francis +1
Robot learning approaches such as behavior cloning and reinforcement learning have shown great promise in synthesizing robot skills from human demonstrations in specific environmen…
SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation
Benjamin Stoler, Ingrid Navarro, Jonathan Francis +1
Verification and validation of autonomous driving (AD) systems and components is of increasing importance, as such technology increases in real-world prevalence. Safety-critical sc…
SafeShift: Safety-Informed Distribution Shifts for Robust Trajectory Prediction in Autonomous Driving
Benjamin Stoler, Ingrid Navarro, Meghdeep Jana +3
As autonomous driving technology matures, safety and robustness of its key components, including trajectory prediction, is vital. Though real-world datasets, such as Waymo Open Mot…