Search

ISR Lab

Masayoshi Tomizuka

Learn to Grasp with Less Supervision: A Data-Efficient Maximum Likelihood Grasp Sampling Loss
Constrained Iterative LQG for Real-Time Chance-Constrained Gaussian Belief Space Planning
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
Enable Faster and Smoother Spatio-Temporal Trajectory Planning for Autonomous Vehicles in Constrained Dynamic Environment
Interpretable End-to-End Urban Autonomous Driving with Latent Deep Reinforcement Learning
Motion Planning for Autonomous Driving With Extended Constrained Iterative LQR
End-to-End Autonomous Driving Perception with Sequential Latent Representation Learning
Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety
Model-Free Deep Reinforcement Learning for Urban Autonomous Driving
Adaptive Probabilistic Vehicle Trajectory Prediction Through Physically Feasible Bayesian Recurrent Neural Network
Autonomous Driving Motion Planning With Constrained Iterative LQR
FOAD: Fast Optimization-based Autonomous Driving Motion Planner
Deep Hierarchical Reinforcement Learning for Autonomous Driving with Distinct Behaviors
Continuous Decision Making for On-Road Autonomous Driving under Uncertain and Interactive Environments
Constrained Iterative LQR for On-Road Autonomous Driving Motion Planning
Spatially-Partitioned Environmental Representation and Planning Architecture for On-Road Autonomous Driving
The Robustly-Safe Automated Driving System for Enhanced Active Safety