title: Agile Continuous Jumping in Discontinuous Terrains
summary: 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/.
paper_authors._id: 66ea385e2451cadfb24fd197
paper_authors.name: Yuxiang Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-18T09:05:47.148Z
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paper_authors.avatarUrl: /avatars/a8553246aae4492b042aa1260938dbe3.svg
paper_authors.fullname: Yuxiang Yang
paper_authors.type: user
paper_authors.user: yxyang
paper_authors._id: 66ea385e2451cadfb24fd198
paper_authors.name: Guanya Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:52:06.802Z
paper_authors._id: 65bbcbadadc958a7d9ef8914
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paper_authors.fullname: Guanya Shi
paper_authors.type: user
paper_authors.user: guanya
paper_authors._id: 66ea385e2451cadfb24fd199
paper_authors.name: Changyi Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-19T07:07:31.392Z
paper_authors._id: 663d635261d90caa6c4d0abf
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paper_authors.fullname: Changyi Lin
paper_authors.type: user
paper_authors.user: linchangyi1
paper_authors._id: 66ea385e2451cadfb24fd19a
paper_authors.name: Xiangyun Meng
paper_authors._id: 66ea385e2451cadfb24fd19b
paper_authors.name: Rosario Scalise
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:52:52.246Z
paper_authors._id: 64335a27fc29acb96beb867d
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paper_authors.fullname: Rosario Scalise
paper_authors.type: user
paper_authors.user: romesco
paper_authors._id: 66ea385e2451cadfb24fd19c
paper_authors.name: Mateo Guaman Castro
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:52:58.621Z
paper_authors._id: 65ee8a6185cb3a3386067258
paper_authors.avatarUrl: /avatars/9eda27b017d7b441d6e9cc4a37debf67.svg
paper_authors.fullname: Mateo Guaman Castro
paper_authors.type: user
paper_authors.user: mateoguaman
paper_authors._id: 66ea385e2451cadfb24fd19d
paper_authors.name: Wenhao Yu
paper_authors._id: 66ea385e2451cadfb24fd19e
paper_authors.name: Tingnan Zhang
paper_authors._id: 66ea385e2451cadfb24fd19f
paper_authors.name: Ding Zhao
paper_authors._id: 66ea385e2451cadfb24fd1a0
paper_authors.name: Jie Tan
paper_authors._id: 66ea385e2451cadfb24fd1a1
paper_authors.name: Byron Boots
paper_title: Agile Continuous Jumping in Discontinuous Terrains
paper_summary: 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/.
paper_ai_keywords: hierarchical learning and control framework heightmap predictor reinforcement-learning-based centroidal-level motion policy model-based leg controller sim-to-real gap hardware characteristics agile and continuous jumps human-sized stairs sparse stepping stones parkour tasks jumping over single horizontal or vertical discontinuities