Simulation & transfer
Motion retargeting
Motion retargeting is the adaptation of a recorded or generated motion from one body to another with different proportions, joints or limits. For humanoid robots, it maps source poses or trajectories into robot configurations while preserving task-relevant relationships such as contacts and end-effector paths and satisfying kinematic, balance, collision and actuator constraints.
Also known as: motion retargetting
Updated
From source motion to target body
Directly copying joint angles rarely works when the source human, character or robot has different limb lengths and joint structure. Motion retargeting instead preserves selected properties of the motion while solving for a feasible target trajectory. The foundational Gleicher method treated important spatial and temporal features as constraints when adapting motion between articulated characters.
The source may be motion-capture markers, estimated human poses, animation or another robot's trajectory. The output is usually a sequence of target poses or joint configurations, sometimes accompanied by contact labels or reference velocities.
Humanoid constraints
For a humanoid, visual similarity is insufficient. Feet must make and break contact at plausible times, hands must reach task-relevant locations, the body must avoid self-collision, and joint position, velocity and torque limits must be respected. Balance and contact forces can make a geometrically close pose physically impossible.
An AIST optimisation formulation jointly considered spatial relationships, dynamic balance and torque limits. Online approaches have also enforced joint, self-collision and balance constraints while mapping tracked human motion to a humanoid, as demonstrated in markerless retargeting to ASIMO.
Role in robot learning
Retargeted trajectories can become reference motions for imitation learning, reinforcement learning or whole-body controllers. They are generated data, not evidence that the physical robot can execute the motion unaided.
A controller still has to track the reference under real dynamics and disturbances. Good datasets therefore preserve the source motion, target robot model, coordinate conventions, contacts, optimisation settings and feasibility checks rather than publishing only the final joint sequence.
Sources
Related terms
Data & collection
Motion capture
Motion capture is the measurement and reconstruction of a person’s or object’s movement over time, commonly as joint positions, orientations or a fitted body model. Optical markers, cameras and inertial sensors can supply the measurements. Humanoid robotics uses the resulting motion sequences for analysis, imitation and retargeting to a robot body.
Models & learning
Imitation learning
Imitation learning is a family of methods that learns a policy from examples of expert behaviour rather than specifying every control rule by hand. In robotics, demonstrations pair observations or states with actions, trajectories or inferred objectives. Behaviour cloning is one imitation-learning method; interactive and inverse approaches address different supervision and distribution-shift problems.
Hardware & control
Whole-body control
Whole-body control coordinates a humanoid’s full multibody state, actuated joints and environmental contacts to pursue several motion or force objectives while respecting constraints such as balance, joint limits and friction. It is a family of hierarchical or optimisation-based methods, not one algorithm; implementations may output joint positions, accelerations or torques.
Data & collection
Trajectory
A trajectory is a time-ordered sequence of states or observations, actions and, where applicable, rewards generated as an agent or robot evolves. A complete episode or policy rollout often yields a trajectory, but the terms are not universally identical: trajectories may be partial, while episodes have dataset- or environment-defined boundaries.