Top Tweets for #DifferentiableSimulation

over 1 year ago

Meet us this week at #CORL2024 in Munich! We will present several papers on #DifferentiableSimulation, #eventcameras, and #ReinforcementLearning at the main conference and workshops! Full list with times, rooms, and links to PDFs, Code, and Videos at: https://t.co/ipsVQ157ZP With Jiaxu Xing, Yunlong Song, Anish Bhattacharya, Elie Aljalbout! @jixing24 @realyunlong @EliJalbout @anishb1010

davsca1's tweet photo. Meet us this week at #CORL2024 in Munich! We will present several papers on #DifferentiableSimulation, #eventcameras, and #ReinforcementLearning at the main conference and workshops! Full list with times, rooms, and links to PDFs, Code, and Videos at: https://t.co/ipsVQ157ZP

With Jiaxu Xing, Yunlong Song, Anish Bhattacharya, Elie Aljalbout! @jixing24 @realyunlong @EliJalbout @anishb1010

Davide Scaramuzza

@davsca1

almost 2 years ago

Congratulations to my student Yunlong Song @realyunlong for successfully defending his Ph.D. in “Learning Robot Control: From #ReinforcementLearning to #DifferentiableSimulation”! Many thanks to the external reviewers Yuke Zhu @yukez from the U. of Texas at Austin, Marco Hutter from ETH Zurich @leggedrobotics, and Martin Riedmiller from @GoogleDeepMind! Yunlong's contributions are: - To show that Reinforcement Learning (RL) outperforms Optimal Control in autonomous racing because it directly optimizes a non-differentiable task-level objective. - To propose a policy-search-for-model-predictive-control (MPC) framework, combining RL's ability to optimize high-level task objectives with MPC's precise actuation and constraint handling. - To introduce a differentiable simulation framework to leverage robot dynamics for more stable and efficient policy training. - To develop a high-performance drone racing system that outperforms optimal control methods and professional pilots. - To develop Flightmare, a flexible modular quadrotor simulator for reinforcement learning and vision-based flight. - Video Recording of the PhD defense: https://t.co/thTme2uoVj - Yunlong's webpage (publications, source code, slides): https://t.co/fGuMakNzgR - Google Scholar: https://t.co/TFMRJ5mA12 Congratulations, Yunlong; it has been an honor to work with you! @UZH_en @UZH_Science @UZHspacehub @ERC_Research @nccrrobotics

davsca1's tweet photo. Congratulations to my student Yunlong Song @realyunlong for successfully defending his Ph.D. in “Learning Robot Control: From #ReinforcementLearning to #DifferentiableSimulation”! Many thanks to the external reviewers Yuke Zhu @yukez from the U. of Texas at Austin, Marco Hutter from ETH Zurich @leggedrobotics, and Martin Riedmiller from @GoogleDeepMind! Yunlong's contributions are:
- To show that Reinforcement Learning (RL) outperforms Optimal Control in autonomous racing because it directly optimizes a non-differentiable task-level objective.
- To propose a policy-search-for-model-predictive-control (MPC) framework, combining RL's ability to optimize high-level task objectives with MPC's precise actuation and constraint handling.
- To introduce a differentiable simulation framework to leverage robot dynamics for more stable and efficient policy training.
- To develop a high-performance drone racing system that outperforms optimal control methods and professional pilots.
- To develop Flightmare, a flexible modular quadrotor simulator for reinforcement learning and vision-based flight.

- Video Recording of the PhD defense: https://t.co/thTme2uoVj
- Yunlong's webpage (publications, source code, slides): https://t.co/fGuMakNzgR
- Google Scholar: https://t.co/TFMRJ5mA12

Congratulations, Yunlong; it has been an honor to work with you!

@UZH_en @UZH_Science @UZHspacehub @ERC_Research @nccrrobotics

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Jiequn Han

@JiequnH

about 3 years ago

A new blog diving into #DifferentiableSimulation #OptimalControl #AI. Explore our work on "Improving Gradient Computation for Differentiable Physics Simulation with Contacts" in this enlightening post by my collaborator @ZhongDesmond: https://t.co/72fWJEBXQN

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