Search Results for author:
Found 8 papers, 3 papers with code
The Waymo Open Sim Agents Challenge
Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development.
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator.
Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping
Our network predicts, for each pixel in an input image, the z position that a robot's end effector would reach if it attempted to grasp or poke at the corresponding position.
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
Imitation learning is an approach for generating intelligent behavior when the cost function is unknown or difficult to specify.
Multi-Agent Imitation Learning for Driving Simulation
Simulation is an appealing option for validating the safety of autonomous vehicles.
Burn-In Demonstrations for Multi-Modal Imitation Learning
Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data.
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems.
