Search Results for author: Blazej Osinski

Found 5 papers, 3 papers with code

Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning

no code implementations16 Oct 2023 Dhruv Shah, Michael Equi, Blazej Osinski, Fei Xia, Brian Ichter, Sergey Levine

Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly discovering a path to a desired goal in unfamiliar settings with such methods often requires lengthy mapping and exploration.

Language Modelling Navigate

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

1 code implementation10 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.

Instruction Following Language Modelling

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

no code implementations26 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

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

1 code implementation26 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.

Model-Based Reinforcement Learning for Atari

2 code implementations1 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

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