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Zhang et al. Intell Robot 2022;2(3):275­97
https://github.com/PaddlePaddle/PaddleRobotics/tree/main/QuadrupedalRobots/ETGRL
https://github.com/Mehooz/vision4leg
Friction,
Friction,
Sensor
Adaptation,and
Noisy
Lateral
Foot
Mass, Per- Velocity https://github.com/Alescontrela/AMP_for_hardware Terrin, (Rough / and Training, Ini- Mass, (Robot Force, External / Joint Mass, of Centre / loss, Friction and I http://dx.doi.org/10.20517/ir.2022.20 Page 297
and
Base Randomisation Curriculum Termination. Randomisation Orientation, Mass, Damping Limit). Inertia, Domain Latency, Kd), Kp,
(Friction, Multiplier). Force), Trajectory Velocity, FrictionCoefficient, CurriculumLearning, andRandomized (Body Joint Torque and KD, (KP, Mass,MotorFrictionandStrength,andSensorLatency),and Setting, (Control Motor and
Randomization Gain Motor Domain Model, External Domain Model, and Position Sampling. Randomization Reference, and Gain, P Friction, Randomization Input. Depth Learning Randomization Mass, Leg

Domain turbation, Actuator Disturbances, Joint-Velocity-Based Actuator Joint tial Height Domain Position Geom Domain Random Teacher-Student Domain Mass, Base Input.

Motion RL another controller. walking Genera-

Shepherd from Data MPC an Generator. Trajectory dog of Dataset behaviors [111] . Trajectory

German Dataset [119] . Motion or policy Foot MoCap turning and / Evolutionary tor [120] .

Make Multi- in Priors loco- robots Learn- Movement Animal with Gener- for

Priors through Learning [56] perceptive and Vision-Guided Locomotion Cross-Modal Learning Approach Locomotion [62]

Motion GoodSubsitutesforComplexRe- Functions [91] Skills Motion robust quadrupedal Repurpose: Robot Human with Trajectory General

Adversarial ward Advanced Adversarial ple Reinforcement Learning for motion wild [8] the in and Imitate Reusable ing From Skills Behaviors [12] Learning Quadrupedal End-to-End Transformers [13] Reinforcement Evolutionary A ator: Quadrupedal