Канал: MPCRL 2021

Patrick Wensing (University of Notre Dame): Tailoring model complexity in MPC of legged locomotion

Patrick Wensing (University of Notre Dame): Tailoring model complexity in MPC of legged locomotion

Nicolas Heess (DeepMind): Towards embodied intelligence

Nicolas Heess (DeepMind): Towards embodied intelligence

Majid Khadiv (MPI): Model and data, two essential ingredients for controlling legged robots

Majid Khadiv (MPI): Model and data, two essential ingredients for controlling legged robots

Daniel Holden (Ubisoft): Robotic characters in video games

Daniel Holden (Ubisoft): Robotic characters in video games

Gerardo Bledt (MIT): Generalizing and improving regularized predictive control for legged robots

Gerardo Bledt (MIT): Generalizing and improving regularized predictive control for legged robots

Jonathan W. Hurst: Learning legged locomotion, RL as one tool in an engineered system

Jonathan W. Hurst: Learning legged locomotion, RL as one tool in an engineered system

Jemin Hwangbo (KAIST): Large-scale policy training for robots

Jemin Hwangbo (KAIST): Large-scale policy training for robots

Michiel van de Panne (UBC): MPC and RL, two different roads to legged locomotion, and that's OK

Michiel van de Panne (UBC): MPC and RL, two different roads to legged locomotion, and that's OK

Teaser abstract #15

Teaser abstract #15

Teaser abstract #9

Teaser abstract #9

Teaser abstract 14

Teaser abstract 14

Teaser abstract #13

Teaser abstract #13

Teaser abstract #7

Teaser abstract #7

Teaser abstract #11

Teaser abstract #11

Teaser abstract #10

Teaser abstract #10

Teaser abstract #5

Teaser abstract #5

Teaser abstract #4

Teaser abstract #4

Teaser abstract #3

Teaser abstract #3

Teaser abstract #2

Teaser abstract #2

Teaser abstract #1

Teaser abstract #1