Search Results for author:
Found 37 papers, 5 papers with code
Holistic Surgical Phase Recognition with Hierarchical Input Dependent State Space Models
Surgical workflow analysis is essential in robot-assisted surgeries, yet the long duration of such procedures poses significant challenges for comprehensive video analysis.
Shared Autonomy for Proximal Teaching
We use this to design Z-COACH, a method for using shared autonomy to provide personalized instruction targeting interpretable task sub-skills.
Generating Out-Of-Distribution Scenarios Using Language Models
In this paper, we leverage these LLM strengths to introduce a framework for generating diverse OOD driving scenarios.
Learning autonomous driving from aerial imagery
In this work, we consider the problem of learning end to end perception to control for ground vehicles solely from aerial imagery.
Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing
Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing.
Probing Multimodal LLMs as World Models for Driving
We provide a sober look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving, challenging common assumptions about their ability to interpret dynamic driving scenarios.
Blending Data-Driven Priors in Dynamic Games
We formulate KLGame, an algorithm for solving non-cooperative dynamic game with Kullback-Leibler (KL) regularization with respect to a general, stochastic, and possibly multi-modal reference policy.
Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery
Understanding and anticipating intraoperative events and actions is critical for intraoperative assistance and decision-making during minimally invasive surgery.
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents.
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning
Advancements in simulation and formal methods-guided environment sampling have enabled the rigorous evaluation of machine learning models in a number of safety-critical scenarios, such as autonomous driving.
Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction
At a certain time, to forecast a reasonable future trajectory, each agent needs to pay attention to the interactions with only a small group of most relevant agents instead of unnecessarily paying attention to all the other agents.
Concept Graph Neural Networks for Surgical Video Understanding
We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see.
Trajectory Prediction with Linguistic Representations
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions.
TIP: Task-Informed Motion Prediction for Intelligent Vehicles
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples.
MAAD: A Model and Dataset for "Attended Awareness" in Driving
Our model takes as input scene information in the form of a video and noisy gaze estimates, and outputs visual saliency, a refined gaze estimate, and an estimate of the person's attended awareness.
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction.
Risk Conditioned Neural Motion Planning
In this paper, we propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic and produce risk-bounded policies efficiently by adding an extra risk term in the loss function of the policy network.
SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic and Robotic Surgery
Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation.
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads.
Behaviorally Diverse Traffic Simulation via Reinforcement Learning
Traffic simulators are important tools in autonomous driving development.
MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
Reasoning about human motion is a core component of modern human-robot interactive systems.
Aggregating Long-Term Context for Learning Laparoscopic and Robot-Assisted Surgical Workflows
With the understanding of the complete surgical workflow, the robots are able to assist the surgeons in intra-operative events, such as by giving a warning when the surgeon is entering specific keys or high-risk phases.
Driving Through Ghosts: Behavioral Cloning with False Positives
In this work, we propose a behavioral cloning approach that can safely leverage imperfect perception without being conservative.
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
To address driving in near-accident scenarios, we propose a hierarchical reinforcement and imitation learning (H-ReIL) approach that consists of low-level policies learned by IL for discrete driving modes, and a high-level policy learned by RL that switches between different driving modes.
CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy
Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems.
Deep Context Maps: Agent Trajectory Prediction using Location-specific Latent Maps
Our main contribution is the concept of learning context maps to improve the prediction task.
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems.
Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections
In this paper, we propose a variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors.
Variational End-to-End Navigation and Localization
We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map.
A Nonparametric Model for Multimodal Collaborative Activities Summarization
We demonstrate the utility of this model for inference tasks such as activity detection, classification, and summarization.
Information-Driven Adaptive Structured-Light Scanners
We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion.
Real-Time Depth Refinement for Specular Objects
The introduction of consumer RGB-D scanners set off a major boost in 3D computer vision research.
RGBD-Fusion: Real-Time High Precision Depth Recovery
The popularity of low-cost RGB-D scanners is increasing on a daily basis.
Coresets for k-Segmentation of Streaming Data
We consider the problem of computing optimal segmentation of such signals by k-piecewise linear function, using only one pass over the data by maintaining a coreset for the signal.
Aerial Reconstructions via Probabilistic Data Fusion
We propose an integrated probabilistic model for multi-modal fusion of aerial imagery, LiDAR data, and (optional) GPS measurements.
A Mixture of Manhattan Frames: Beyond the Manhattan World
Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system.
