title: Berkeley Humanoid: A Research Platform for Learning-based Control
summary: We introduce Berkeley Humanoid, a reliable and low-cost mid-scale humanoid
  research platform for learning-based control. Our lightweight, in-house-built
  robot is designed specifically for learning algorithms with low simulation
  complexity, anthropomorphic motion, and high reliability against falls. The
  robot's narrow sim-to-real gap enables agile and robust locomotion across
  various terrains in outdoor environments, achieved with a simple reinforcement
  learning controller using light domain randomization. Furthermore, we
  demonstrate the robot traversing for hundreds of meters, walking on a steep
  unpaved trail, and hopping with single and double legs as a testimony to its
  high performance in dynamical walking. Capable of omnidirectional locomotion
  and withstanding large perturbations with a compact setup, our system aims for
  scalable, sim-to-real deployment of learning-based humanoid systems. Please
  check http://berkeley-humanoid.com for more details.
paper_authors._id: 66aaf75f9a318d1bc02a3ce1
paper_authors.name: Qiayuan Liao
paper_authors._id: 66aaf75f9a318d1bc02a3ce2
paper_authors.name: Bike Zhang
paper_authors._id: 66aaf75f9a318d1bc02a3ce3
paper_authors.name: Xuanyu Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-02T15:42:35.814Z
paper_authors._id: 62270596a5e47a80eebc0eb4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1646724490237-noauth.jpeg
paper_authors.fullname: Xuanyu Huang
paper_authors.type: user
paper_authors.user: xiplus
paper_authors._id: 66aaf75f9a318d1bc02a3ce4
paper_authors.name: Xiaoyu Huang
paper_authors._id: 66aaf75f9a318d1bc02a3ce5
paper_authors.name: Zhongyu Li
paper_authors._id: 66aaf75f9a318d1bc02a3ce6
paper_authors.name: Koushil Sreenath
paper_title: Berkeley Humanoid: A Research Platform for Learning-based Control
paper_summary: We introduce Berkeley Humanoid, a reliable and low-cost mid-scale humanoid
  research platform for learning-based control. Our lightweight, in-house-built
  robot is designed specifically for learning algorithms with low simulation
  complexity, anthropomorphic motion, and high reliability against falls. The
  robot's narrow sim-to-real gap enables agile and robust locomotion across
  various terrains in outdoor environments, achieved with a simple reinforcement
  learning controller using light domain randomization. Furthermore, we
  demonstrate the robot traversing for hundreds of meters, walking on a steep
  unpaved trail, and hopping with single and double legs as a testimony to its
  high performance in dynamical walking. Capable of omnidirectional locomotion
  and withstanding large perturbations with a compact setup, our system aims for
  scalable, sim-to-real deployment of learning-based humanoid systems. Please
  check http://berkeley-humanoid.com for more details.
paper_ai_keywords: reinforcement learning domain randomization sim-to-real agile locomotion robust locomotion anthropomorphic motion high reliability falls omnidirectional locomotion perturbations