DreamWaQ
Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning
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I Made Aswin Nahrendra, Byeongho Yu, and Hyun Myung
Urban Robotics Laboratory
School of Electrical Engineering, KAIST
[Paper] [Code] (Coming soon)
Experiments
Experiments
Deformable slopes
Deformable slopes
On deformable slopes, DreamWaQ's policy can rapidly adapt to uncertainties such as foot slipping.
Thick vegetation
Thick vegetation
When faced with thick vegetation, DreamWaQ's policy can adjust its joint power to overcome trapping due to the vegetation.
Pedestrian curb
Pedestrian curb
DreamWaQ's policy can easily adapt its gait pattern to overcome a 15cm-height pedestrian curb
Stairs
Stairs
On medium-rise stairs, DreamWaQ's policy enables a relatively small A1 robot to go down and up the stairs without any mode change
Outdoor Long-walk Course
Outdoor Long-walk Course
The experiment was conducted after rainfall, which made the stairs more slippery. Moreover, some natural terrains also turned into mud, making the robot's feet stepped deeper on it.
The hiking course has an elevation gain of up to 22m from the robot's starting point and a maximum slope of up to 36 degree.
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