research
∙
07/10/2022
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
Goal-conditioned policies for robotic navigation can be trained on large...
share
research
∙
11/22/2021
Off-Policy Correction For Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) provides a framework for probl...
research
∙
09/28/2021
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
In this paper we present the first safe system for full control of self-...
research
∙
09/27/2021
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
In this work we are the first to present an offline policy gradient meth...
research
∙
05/26/2021
What data do we need for training an AV motion planner?
We investigate what grade of sensor data is required for training an imi...
research
∙
05/26/2021
SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
In this work, we present a simple end-to-end trainable machine learning ...
research
∙
12/16/2020
CARLA Real Traffic Scenarios – novel training ground and benchmark for autonomous driving
This work introduces interactive traffic scenarios in the CARLA simulato...
research
∙
11/29/2019
Simulation-based reinforcement learning for real-world autonomous driving
We use synthetic data and a reinforcement learning algorithm to train a ...
research
∙
03/01/2019
Model-Based Reinforcement Learning for Atari
Model-free reinforcement learning (RL) can be used to learn effective po...
research
∙
04/02/2018
