1 citations · 1 across the 10 of their papers we have counts for
11 papers
The Unexpired Plan: A Free Monitor for Accelerated Diffusion Policies
Yi Zhao, Sebastian Scherer
Training-free acceleration of a diffusion policy is accepted when an internal similarity signal reports that the shortcut changed nothing. We price every monitor a control loop can…
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
Bowen Li, Mayank Mishra, Y. Isabel Liu +7
Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforcement learning (RL) can learn re…
World2Rules: A Neuro-Symbolic Framework for Learning World-Governing Safety Rules for Aviation
Haichuan Wang, Jay Patrikar, Sebastian Scherer
Many real-world safety-critical systems are governed by explicit rules that define unsafe world configurations and constrain agent interactions. In practice, these rules are comple…
GrndCtrl: Grounding World Models via Self-Supervised Reward Alignment
Haoyang He, Jay Patrikar, Dong-Ki Kim +5
Recent advances in video world modeling have enabled large-scale generative models to simulate embodied environments with high visual fidelity, providing strong priors for predicti…
Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers
Yutian Chen, Yuheng Qiu, Ruogu Li +4
We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the base model. Co-Me distilled a l…
The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving
Jay Patrikar, Apoorva Sharma, Sushant Veer +3
Learning-based autonomous driving systems are trained mostly on incident-free data, offering little guidance near safety-performance boundaries. Real crash reports contain precisel…
AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Rebecca Martin, Jay Patrikar, Sebastian Scherer
Specialized machine learning models, regardless of architecture and training, are susceptible to failures in deployment. With their increasing use in high risk situations, the abil…
Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid
Parv Kapoor, Ian Higgins, Nikhil Keetha +9
Assured safe-separation is essential for achieving seamless high-density operation of airborne vehicles in a shared airspace. To equip resource-constrained aerial systems with this…
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments
Devansh Dhrafani, Yifei Liu, Andrew Jong +6
Robust depth perception in visually-degraded environments is crucial for autonomous aerial systems. Thermal imaging cameras, which capture infrared radiation, are robust to visual…
Amelia: A Large Dataset and Benchmark for Airport Surface Movement Forecasting
Ingrid Navarro, Pablo Ortega-Kral, Jay Patrikar +6
Demand for air travel is rising, straining existing aviation infrastructure. In the US, more than 90% of airport control towers are understaffed, falling short of FAA and union sta…
RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts
Jay Patrikar, Sushant Veer, Apoorva Sharma +2
Modern motion planners for autonomous driving frequently use imitation learning (IL) to draw from expert driving logs. Although IL benefits from its ability to glean nuanced and mu…