@m.vandepanne
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Michiel van de Panne | ALLSTEPS: Curriculum-driven Learning of Stepping Stone skills @m.vandepanne | Uploaded May 2020 | Updated October 2024, 2 hours ago.
This video accompanies the paper of the same title.
Curriculum-driven deep reinforcement learning is used to learn control policies for physics-based characters, enabling them to walk across stepping stone sequences and variable terrain. The control policy perceives the upcoming two desired stepping locations.
ALLSTEPS:  Curriculum-driven Learning of Stepping Stone skillsSCA 2020:  ALLSTEPS Curriculum-driven Learning of Stepping Stone Skills (full talk)Dynamic Terrain Traversal Skills Using Reinforcement Learning (part 1)Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement LearningFeedback Control for Cassie with Deep Reinforcement LearningProgressive Reinforcement Learning with Distillation for Multi-Skilled Motion ControlDynamic Animation Synthesis with Free-Form DeformationsPartwiseMPC: Interactive Control of Contact-Guided MotionsSIGGRAPH 2017 DeepLoco (supplemental video)2005 sbim sketch3dTask-based Locomotion (SIGGRAPH 2016)

ALLSTEPS: Curriculum-driven Learning of Stepping Stone skills @m.vandepanne

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