1 code implementation • 15 Jun 2023 • Zhehui Huang, Sumeet Batra, Tao Chen, Rahul Krupani, Tushar Kumar, Artem Molchanov, Aleksei Petrenko, James A. Preiss, Zhaojing Yang, Gaurav S. Sukhatme
In addition to speed, such simulators need to model the physics of the robots and their interaction with the environment to a level acceptable for transferring policies learned in simulation to reality.
no code implementations • 18 Feb 2023 • Anton Kolonin, Andrey Kurpatov, Artem Molchanov, Gennadiy Averyanov
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory.
no code implementations • 17 Apr 2022 • Anton Kolonin, Andrey Kurpatov, Artem Molchanov, Gennadiy Averyanov
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory.
no code implementations • 13 Feb 2022 • Evgenii Vityaev, Anton Kolonin, Andrey Kurpatov, Artem Molchanov
In this paper, for the description and modeling of Brain Principles Programming, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories.
no code implementations • 23 Sep 2021 • Alexey Kamenev, Lirui Wang, Ollin Boer Bohan, Ishwar Kulkarni, Bilal Kartal, Artem Molchanov, Stan Birchfield, David Nistér, Nikolai Smolyanskiy
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving.
3 code implementations • 3 Oct 2019 • Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.
no code implementations • 25 Sep 2019 • Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier
We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.
1 code implementation • 12 Jun 2019 • Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier
This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.
2 code implementations • 11 Mar 2019 • Artem Molchanov, Tao Chen, Wolfgang Hönig, James A. Preiss, Nora Ayanian, Gaurav S. Sukhatme
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation.
Robotics
no code implementations • 4 Jul 2018 • Artem Molchanov, Karol Hausman, Stan Birchfield, Gaurav Sukhatme
In this work, we introduce a method based on region-growing that allows learning in an environment with any pair of initial and goal states.
1 code implementation • 18 May 2018 • Jonathan Tremblay, Thang To, Artem Molchanov, Stephen Tyree, Jan Kautz, Stan Birchfield
We present a system to infer and execute a human-readable program from a real-world demonstration.
Robotics