Search Results for author:
Found 11 papers, 6 papers with code
Driving Everywhere with Large Language Model Policy Adaptation
Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs).
MISFIT-V: Misaligned Image Synthesis and Fusion using Information from Thermal and Visual
Detecting humans from airborne visual and thermal imagery is a fundamental challenge for Wilderness Search-and-Rescue (WiSAR) teams, who must perform this function accurately in the face of immense pressure.
WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue
Unfortunately, visual sensors alone do not address the need for robustness across all the possible terrains, weather, and lighting conditions that WiSAR operations can be conducted in.
HALO: Hazard-Aware Landing Optimization for Autonomous Systems
With autonomous aerial vehicles enacting safety-critical missions, such as the Mars Science Laboratory Curiosity rover's landing on Mars, the tasks of automatically identifying and reasoning about potentially hazardous landing sites is paramount.
Towards Data-Driven Synthesis of Autonomous Vehicle Safety Concepts
As safety-critical autonomous vehicles (AVs) will soon become pervasive in our society, a number of safety concepts for trusted AV deployment have recently been proposed throughout industry and academia.
Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions
To achieve seamless human-robot interactions, robots need to intimately reason about complex interaction dynamics and future human behaviors within their motion planning process.
Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms.
Backpropagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods
This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs.
Better AI through Logical Scaffolding
We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components.
Generative Modeling of Multimodal Multi-Human Behavior
This work presents a methodology for modeling and predicting human behavior in settings with N humans interacting in highly multimodal scenarios (i. e. where there are many possible highly-distinct futures).
Robotics Human-Computer Interaction
Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction
This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i. e., the possibility of multiple highly distinct futures, plays a critical role in decision making.
