Berkeley Humanoid: A Research Platform for Learning-based Control
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.
66aaf75f9a318d1bc02a3ce1
Qiayuan Liao
66aaf75f9a318d1bc02a3ce2
Bike Zhang
66aaf75f9a318d1bc02a3ce3
Xuanyu Huang
admin_assigned
2024-08-02T15:42:35.814Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1646724490237-noauth.jpeg
Xuanyu Huang
user
xiplus
66aaf75f9a318d1bc02a3ce4
Xiaoyu Huang
66aaf75f9a318d1bc02a3ce5
Zhongyu Li
66aaf75f9a318d1bc02a3ce6
Koushil Sreenath
Berkeley Humanoid: A Research Platform for Learning-based Control
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.
Berkeley Humanoid, a mid-scale humanoid robot, achieves robust and agile locomotion using reinforcement learning with light domain randomization, demonstrating performance across various terrains and perturbations.
learning-based control reinforcement learning domain randomization dynamical walking omnidirectional locomotion