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https://github.com/lauramsmith/fine-tuning-locomotion
https://github.com/leggedrobotics/legged_gym
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Modelling, Ini- Shifting / Scaling, Torque Actuator / / / Zhang et al. Intell Robot 2022;2(3):275­97 Scaling, Torque Actuator I http://dx.doi.org/10.20517/ir.2022.20
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TOWR [113] , FMM [117] , / / Human / Trajectory rithm [118] . Dog MoCap Side-Step Gaits Dynamic Curriculum /

Adapta- Locomo- Reinforcement Legged Consump- of Emergence Adaptation of Control for Control Reinforcement Locomo- from on Keep Loco- Real the Legged Reinforce- Optimal Us- Minutes 4 https://github.com/IntelRealSense/librealsense

Trajectory Quadrupedal Deep Coupling Vision and Propriocep- of navigation Energy the to Robots [107] Legged Motor Robots [60] Motion Quadrupedal Robots using Deep Learning [108] Torque Locomotion [44] Robust for Demonstrations Optimization [109] that Robots Fine-Tuning in Policies Terrain-Aware using and Learning RapidLocomotionviaReinforce- Learning [36] in Walk ing Massively Parallel Deep Rein- Learning [35]

Real-Time for tion using tion Learning [53] for tion Robots [11] Minimizing Leads tion in Gaits Rapid RMA: Legged for Human Reinforcement Learning Quadrupedal Model-free Learning using tion Trajectory Legged Learning: motion World [30] RLOC: Locomotion ment Control [42] ment to Learning forcement