LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action

1 code implementation • 10 Jul 2022 • Dhruv Shah, Blazej Osinski, Brian Ichter, Sergey Levine

Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings.

Association Instruction Following +1

SimNet: Learning Reactive Self-driving Simulations from Real-world Observations

no code implementations • 26 May 2021 • Luca Bergamini, Yawei Ye, Oliver Scheel, Long Chen, Chih Hu, Luca Del Pero, Blazej Osinski, Hugo Grimmett, Peter Ondruska

We train our system directly from 1, 000 hours of driving logs and measure both realism, reactivity of the simulation as the two key properties of the simulation.

What data do we need for training an AV motion planner?

no code implementations • 26 May 2021 • Long Chen, Lukas Platinsky, Stefanie Speichert, Blazej Osinski, Oliver Scheel, Yawei Ye, Hugo Grimmett, Luca Del Pero, Peter Ondruska

If cheaper sensors could be used for collection instead, data availability would go up, which is crucial in a field where data volume requirements are large and availability is small.

Imitation Learning Motion Planning

Model-Based Reinforcement Learning for Atari

2 code implementations • 1 Mar 2019 • Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Atari Games 100k +4