From the perspective of application:
Humanoid robots are built to (ideally) replicate human motions in performing various human-level skills, such as locomotion, manipulation, and cognitive capabilities.
From the perspective of composition:
A humanoid robot refers to any anthropomorphic robot that resembles the form of a human. Typically, a humanoid robot possesses a torso, two arms, and two legs, though the degree of anthropomorphism may vary.
Background
Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning lists background, i.e., definition of locomotion, manipulation, loco-manipulation, whole-body control.
Learning Loco-manipulation Skills
Reinforcement Learning from Scratch
Refer to Reinforcement Learning for Humanoid Robots.
Learning from Demonstration
Refer to Imitation Learning for Humanoid Robots.
Hybrid Models
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A teacher policy trained from simulation using pure RL. Then a student policy clones the behavior of the teacher.
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Using IL first to pre-train an policy from demonstration. Then a RL policy fine-tunes the policy.
Representaion of Skills
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motion Representation
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goal representation
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state transition representation
Learning for Humanoid Loco-manipulation
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most in simulation, the physical interactions with external environment or objects are often oversimplified
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careful reward design using RL
Foundation Models for Humanoid Robots
Applying LLMs/VLMs to Humanoid Robots
Refer to Real-World Robot Applications of Foundation Models: A Review.
Building Humanoid Foundation Models
Refer to Robotic Foundation Models for robotic foundation models.