1 citations · 2 across the 3 of their papers we have counts for
4 papers
TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
Momchil S. Tomov, Sang Uk Lee, Hansford Hendrago +14
We present TreeIRL, a novel planner for autonomous driving that combines Monte Carlo tree search (MCTS) and inverse reinforcement learning (IRL) to achieve state-of-the-art perform…
Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving
Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan +8
Machine Learning (ML) has replaced traditional handcrafted methods for perception and prediction in autonomous vehicles. Yet for the equally important planning task, the adoption o…
Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents
Jacopo Tani, Andrea F. Daniele, Gianmarco Bernasconi +10
As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible. Compared to other sciences, there are specific challen…
The AI Driving Olympics at NeurIPS 2018
Julian Zilly, Jacopo Tani, Breandan Considine +14
Despite recent breakthroughs, the ability of deep learning and reinforcement learning to outperform traditional approaches to control physically embodied robotic agents remains lar…