BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds

Traversing risky terrains with sparse footholds poses a significant challenge for humanoid robots, requiring precise foot placements and stable locomotion. Existing approaches designed for quadrupedal robots often fail to generalize to humanoid robots due to differences in foot geometry and unstable morphology, while learning-based approaches for humanoid locomotion still face great challenges on complex terrains due to sparse foothold reward signals and inefficient learning processes. To address these challenges, we introduce BeamDojo, a reinforcement learning (RL) framework designed for enabling agile humanoid locomotion on sparse footholds. BeamDojo begins by introducing a sampling-based foothold reward tailored for polygonal feet, along with a double critic to balancing the learning process between dense locomotion rewards and sparse foothold rewards. To encourage sufficient trail-and-error exploration, BeamDojo incorporates a two-stage RL approach: the first stage relaxes the terrain dynamics by training the humanoid on flat terrain while providing it with task terrain perceptive observations, and the second stage fine-tunes the policy on the actual task terrain. Moreover, we implement a onboard LiDAR-based elevation map to enable real-world deployment. Extensive simulation and real-world experiments demonstrate that BeamDojo achieves efficient learning in simulation and enables agile locomotion with precise foot placement on sparse footholds in the real world, maintaining a high success rate even under significant external disturbances.

(a) Training in Simulation. BeamDojo incorporates a two-stage RL approach.

• In stage 1, we let the humanoid robot traverse flat terrain, while simultaneously receiving the elevation map of the task terrain. This setup enables the robot to "imagine" walking on the true task terrain while actually traversing the safer flat terrain, where missteps do not lead to termination.
• Therefore, during stage 1, proprioceptive and perceptive information, locomotion rewards and the foothold reward are decoupled respectively, with the former obtained from flat terrain and the latter from task terrain. The double-critic module separately learns two reward groups.
• In stage 2, the policy is fine-tuned on the task terrain, utilizing the full set of observations and rewards. The double-critic module undergoes a deep copy.

(b) Deployment. The robot-centric elevation map, reconstructed using LiDAR data, is combined with proprioceptive information to serve as the input for the actor.

Many excellent works inspire the design of BeamDojo.

• Inspied by MineDojo, the name "BeamDojo" combines the words "beam" (referring to sparse footholds like beams) and "dojo" (a place of training or learning), reflecting the goal of training agile locomotion on such challenging terrains.
• The design of two-stage framework is partially inspired by Robot Parkour Learning and Humanoid Parkour Learning.
• The design of double-critic module is inspired by RobotKeyframing.
• The design of training terrain is inspired by Learning Agile Locomotion on Risky Terrains and Walking with Terrain Reconstruction.
• We sincerely thank the authors of PIM: Learning Humanoid Locomotion with Perceptive Internal Model for their kind help with the deployment of the elevation map.