65 citations · 186 across the 23 of their papers we have counts for
24 papers
FCTS: Failure-Guided Curriculum Tree Search Over Generated Terrain for Legged Parkour
Narayanan Palghat Parameswaran, Dong-Ki Kim, Jay Patrikar +4
Quadruped parkour policies handle canonical obstacles well, but their training curricula often miss the irregular, compound geometry of industrial and disaster-response sites, wher…
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…
Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered
Jason Jabbour, Dong-Ki Kim, Max Smith +6
Vision-Language-Action (VLA) models are a promising paradigm for field robotics, but their deployment is bottlenecked by strict edge SWaP-C constraints. Compression techniques such…
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…
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…
TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations
Jay Patrikar, Joao Dantas, Brady Moon +7
We introduce TartanAviation, an open-source multi-modal dataset focused on terminal-area airspace operations. TartanAviation provides a holistic view of the airport environment by…
Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Yafei Hu, Quanting Xie, Vidhi Jain +20
Building general-purpose robots that operate seamlessly in any environment, with any object, and utilizing various skills to complete diverse tasks has been a long-standing goal in…
FoundLoc: Vision-based Onboard Aerial Localization in the Wild
Yao He, Ivan Cisneros, Nikhil Keetha +6
Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GN…
SoRTS: Learned Tree Search for Long Horizon Social Robot Navigation
Ingrid Navarro, Jay Patrikar, Joao P. A. Dantas +4
The fast-growing demand for fully autonomous robots in shared spaces calls for the development of trustworthy agents that can safely and seamlessly navigate in crowded environments…
Pegasus Simulator: An Isaac Sim Framework for Multiple Aerial Vehicles Simulation
Marcelo Jacinto, João Pinto, Jay Patrikar +4
Developing and testing novel control and motion planning algorithms for aerial vehicles can be a challenging task, with the robotics community relying more than ever on 3D simulati…
Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace
Ingrid Navarro, Jay Patrikar, Joao P. A. Dantas +4
The fast-growing demand for fully autonomous aerial operations in shared spaces necessitates developing trustworthy agents that can safely and seamlessly navigate in crowded, dynam…
Challenges in Close-Proximity Safe and Seamless Operation of Manned and Unmanned Aircraft in Shared Airspace
Jay Patrikar, Joao P. A. Dantas, Sourish Ghosh +11
We propose developing an integrated system to keep autonomous unmanned aircraft safely separated and behave as expected in conjunction with manned traffic. The main goal is to achi…
Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains
Jasmine Jerry Aloor, Jay Patrikar, Parv Kapoor +2
Seamlessly integrating rules in Learning-from-Demonstrations (LfD) policies is a critical requirement to enable the real-world deployment of AI agents. Recently, Signal Temporal Lo…
AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking
Sourish Ghosh, Jay Patrikar, Brady Moon +2
Detect-and-Avoid (DAA) capabilities are critical for safe operations of unmanned aircraft systems (UAS). This paper introduces, AirTrack, a real-time vision-only detect and trackin…
Drone flight data reveal energy and greenhouse gas emissions savings for small package delivery
Thiago A. Rodrigues, Jay Patrikar, Natalia L. Oliveira +3
The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will affect the energy productivity of package delivery and require new methods to understand the associate…
Adaptive Safety Margin Estimation for Safe Real-Time Replanning under Time-Varying Disturbance
Cherie Ho, Jay Patrikar, Rogerio Bonatti +1
Safe navigation in real-time is challenging because engineers need to work with uncertain vehicle dynamics, variable external disturbances, and imperfect controllers. A common safe…
Predicting Like A Pilot: Dataset and Method to Predict Socially-Aware Aircraft Trajectories in Non-Towered Terminal Airspace
Jay Patrikar, Brady Moon, Jean Oh +1
Pilots operating aircraft in un-towered airspace rely on their situational awareness and prior knowledge to predict the future trajectories of other agents. These predictions are c…
CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model
Arnav Choudhry, Brady Moon, Jay Patrikar +2
Energy management is a critical aspect of risk assessment for Uncrewed Aerial Vehicle (UAV) flights, as a depleted battery during a flight brings almost guaranteed vehicle damage a…
In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery
Thiago A. Rodrigues, Jay Patrikar, Arnav Choudhry +9
We autonomously direct a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or "drone" to take off, fly a specified route, and land for a total of 209 flights while va…