Agile Continuous Jumping in Discontinuous Terrains
We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal
robots in discontinuous terrains such as stairs and stepping stones. Unlike
single-step jumping, continuous jumping requires accurately executing highly
dynamic motions over long horizons, which is challenging for existing
approaches. To accomplish this task, we design a hierarchical learning and
control framework, which consists of a learned heightmap predictor for robust
terrain perception, a reinforcement-learning-based centroidal-level motion
policy for versatile and terrain-adaptive planning, and a low-level model-based
leg controller for accurate motion tracking. In addition, we minimize the
sim-to-real gap by accurately modeling the hardware characteristics. Our
framework enables a Unitree Go1 robot to perform agile and continuous jumps on
human-sized stairs and sparse stepping stones, for the first time to the best
of our knowledge. In particular, the robot can cross two stair steps in each
jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds.
Moreover, the same policy outperforms baselines in various other parkour tasks,
such as jumping over single horizontal or vertical discontinuities. Experiment
videos can be found at https://yxyang.github.io/jumping\_cod/.
66ea385e2451cadfb24fd197
Yuxiang Yang
claimed_verified
2024-09-18T09:05:47.148Z
6490cd85ed034b1864d4f8e4
/avatars/a8553246aae4492b042aa1260938dbe3.svg
Yuxiang Yang
user
yxyang
66ea385e2451cadfb24fd198
Guanya Shi
admin_assigned
2024-09-18T08:52:06.802Z
65bbcbadadc958a7d9ef8914
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/9Pv8b1nUXIP2fvTS281DK.jpeg
Guanya Shi
user
guanya
66ea385e2451cadfb24fd199
Changyi Lin
claimed_verified
2024-09-19T07:07:31.392Z
663d635261d90caa6c4d0abf
/avatars/aebc16bd138f92160ae729d4cf42121b.svg
Changyi Lin
user
linchangyi1
66ea385e2451cadfb24fd19a
Xiangyun Meng
66ea385e2451cadfb24fd19b
Rosario Scalise
admin_assigned
2024-09-18T08:52:52.246Z
64335a27fc29acb96beb867d
https://cdn-avatars.huggingface.co/v1/production/uploads/64335a27fc29acb96beb867d/lbOLRYxU-maigC7H2IAJv.jpeg
Rosario Scalise
user
romesco
66ea385e2451cadfb24fd19c
Mateo Guaman Castro
admin_assigned
2024-09-18T08:52:58.621Z
65ee8a6185cb3a3386067258
/avatars/9eda27b017d7b441d6e9cc4a37debf67.svg
Mateo Guaman Castro
user
mateoguaman
66ea385e2451cadfb24fd19d
Wenhao Yu
66ea385e2451cadfb24fd19e
Tingnan Zhang
66ea385e2451cadfb24fd19f
Ding Zhao
66ea385e2451cadfb24fd1a0
Jie Tan
66ea385e2451cadfb24fd1a1
Byron Boots
Agile Continuous Jumping in Discontinuous Terrains
We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal
robots in discontinuous terrains such as stairs and stepping stones. Unlike
single-step jumping, continuous jumping requires accurately executing highly
dynamic motions over long horizons, which is challenging for existing
approaches. To accomplish this task, we design a hierarchical learning and
control framework, which consists of a learned heightmap predictor for robust
terrain perception, a reinforcement-learning-based centroidal-level motion
policy for versatile and terrain-adaptive planning, and a low-level model-based
leg controller for accurate motion tracking. In addition, we minimize the
sim-to-real gap by accurately modeling the hardware characteristics. Our
framework enables a Unitree Go1 robot to perform agile and continuous jumps on
human-sized stairs and sparse stepping stones, for the first time to the best
of our knowledge. In particular, the robot can cross two stair steps in each
jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds.
Moreover, the same policy outperforms baselines in various other parkour tasks,
such as jumping over single horizontal or vertical discontinuities. Experiment
videos can be found at https://yxyang.github.io/jumping\_cod/.
A hierarchical learning and control framework enables a quadrupedal robot to perform agile, continuous, and terrain-adaptive jumping in complex environments using a combination of terrain perception, motion planning, and low-level control.
heightmap predictor reinforcement-learning-based centroidal-level motion policy low-level model-based leg controller sim-to-real gap