Robotics
Motion retargeting
Definition
Motion retargeting maps a motion recorded or designed for one body onto another body with different geometry or joints. In robotics it produces a compatible pose or trajectory reference, which still needs a controller to execute it physically.
Updated
Match motion across different bodies
Peng and colleagues retarget animal motions to a robot by pairing body keypoints and solving inverse kinematics so the robot's corresponding points follow the reference. The robot and source animal need not have identical proportions.
The same distinction matters when human motion supplies a humanoid's training reference. Copying recorded joint values directly generally does not account for different joint arrangements and limb lengths.
A reference motion is not an executable policy
The animal-imitation framework separates retargeting from a subsequent reinforcement-learning stage that trains a controller to track the reference in simulation. It then addresses transfer to hardware.
ASAP similarly uses retargeted human motion to pretrain humanoid tracking policies and then uses real-world data to address dynamics mismatch. Its authors evaluate transfer to the Unitree G1.
Geometry and dynamics impose different limits
Retargeting can seek poses that reproduce important features of the source motion. That does not by itself ensure balance, feasible contact forces, or sufficient actuator capability during execution.
When assessing a demonstration, distinguish the source recording, retargeted reference, simulated tracking policy, and physical result. They are separate stages, and success at an earlier stage does not establish success at the later one.
Sources
Related terms
Inverse kinematics
Inverse kinematics finds joint positions that produce a desired robot end-effector position, orientation, or other geometric task. A target can have multiple solutions, no solution, or a continuous family of solutions.
Imitation learning
Imitation learning learns behavior from examples supplied by a demonstrator. In robotics, demonstrations can teach a policy how to perform a task without requiring every action or objective to be programmed by hand.
Cross-embodiment learning
Cross-embodiment learning uses experience from different robot bodies to train representations or policies that can transfer across those bodies. It requires a way to handle differences in sensing, geometry, and available actions.