Why humanoid learning is difficult
Humanoids combine many degrees of freedom with balance, whole-body coordination and contact-rich manipulation. A successful behavior must respect both the task and the robot's physical constraints.
Demonstrations and teleoperation
Human demonstrations provide examples of task intent and successful behavior. Teleoperation can generate robot trajectories while keeping a human in the control loop, making physical interaction easier to capture than hand-written programs.
Imitation learning
Imitation learning trains a policy to reproduce behavior from demonstrations. It can work well for tasks with clear examples, but performance can degrade when the robot reaches states that differ from the training distribution.
Reinforcement learning
Reinforcement learning can optimize behavior using rewards or other objectives. In robotics it is often combined with simulation, demonstrations, constraints or controlled real-world data because unrestricted trial and error is expensive and potentially unsafe.
Whole-body coordination
Humanoid behavior is rarely isolated to one joint or one arm. Walking, reaching, grasping and balance interact, making whole-body control and state estimation important parts of the learning stack.
The embodiment connection
A general skill representation must ultimately become actions compatible with a particular body. Humanoid learning is therefore closely connected to cross-embodiment learning and robot foundation models.
Explore humanoid robots, whole-body control and cross-embodiment learning.