Hardware & control
Robot dynamics
Robot dynamics describes the relationship between forces and torques acting on a robot and the motion they produce, accounting for mass, inertia, gravity, velocity-dependent effects and external contact. Forward dynamics predicts acceleration from applied forces; inverse dynamics computes forces or torques for a specified motion.
Also known as: robotic dynamics
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Dynamics adds the causes of motion
Robot kinematics relates joint values to pose and velocity without asking what caused them. Dynamics adds link mass and inertia, applied force and torque, gravity, friction, compliance and environmental contact. The result is an equation of motion that constrains which trajectories a physical robot can produce.
Modern Robotics Chapter 8 presents the standard rigid-body terms: a configuration-dependent mass matrix, velocity-product effects such as Coriolis and centripetal terms, gravity and external wrenches. Real mechanisms can also require models of motor inertia, gearing, damping, backlash and flexible structures.
Forward and inverse dynamics answer opposite questions
Forward dynamics starts with robot state and applied forces or torques, then calculates acceleration. Integrating those accelerations produces a simulated trajectory. Inverse dynamics starts with state and a requested acceleration, then calculates the joint forces or torques needed to realise it.
The Springer Handbook treatment covers both problems for general robot mechanisms. Inverse dynamics is useful for model-based control, but its answer is only physically achievable when actuator, contact and friction constraints are satisfied.
Humanoids add floating-base and contact dynamics
A humanoid’s root body is not fixed to the world and its contacts change as it walks or manipulates. Ground and object contact forces therefore participate directly in the dynamics. Impacts can cause velocity changes that a smooth kinematic trajectory does not represent.
Dynamic data should identify the robot model and inertial parameters, coordinate and sign conventions, applied and measured torque, external wrench, contact state, payload, timestep and solver assumptions. Simulator state is not automatically hardware ground truth: inaccurate friction, compliance or actuator models can produce motion that the real robot cannot match.
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Related terms
Hardware & control
Robot kinematics
Robot kinematics describes the geometric relationship between a robot’s joint configuration and the position, orientation and velocity of its links or end-effector, without modelling the forces that cause the motion. Forward kinematics computes pose from joint values; inverse kinematics searches for joint values that achieve a requested pose.
Hardware & control
Actuator
An actuator is a robot’s power mechanism that converts electrical, hydraulic, pneumatic or other supplied energy into controlled mechanical force, torque or motion. In a humanoid, actuators drive joints, often through transmissions; they are distinct from the joints and sensors, and their arrangement need not correspond one-to-one with the robot’s degrees of freedom.
Hardware & control
Torque control
Torque control is a control mode in which desired joint torques, or equivalent linear forces, are the command variables sent to a robot’s low-level actuators. Unlike position control, it does not directly command where each joint should be. Motion and contact instead emerge from the applied efforts, robot dynamics, feedback, constraints and any higher-level controller.
Hardware & control
Floating base
A floating-base model represents a robot’s root body with six unconstrained spatial degrees of freedom—three for translation and three for rotation—instead of fixing it to the world. Those base coordinates are not directly actuated; a legged robot changes them through joint motion and external contact forces.
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.