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name: mdp-components description: "Use mjlab built-in actions, actuators, observations, rewards, terminations, events, commands, curricula, metrics, and task-specific MDP terms." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

mdp-components

Use this sub-skill when a task asks which mjlab MDP term, action config, actuator config, command generator, reward, termination, curriculum, or built-in task-family component to use.

Start here

• Read actions-actuators.md for policy action terms, actuator configs, scaling/offset/clip, delay, and differential IK.
• Read mdp-term-catalog.md for built-in observation, reward, termination, event, command, curriculum, and metric functions.
• Read task-specific-mdp.md for velocity, tracking, manipulation, and cartpole term patterns.
• Read troubleshooting.md when actions have the wrong dimension, rewards have the wrong sign, commands are missing, or target regexes fail.
• Run scripts/inspect_mdp_terms.py to list public callables/signatures from installed mjlab MDP modules.

Quick choices

Need	Typical mjlab surface
Drive joints from policy output	JointPositionActionCfg, JointVelocityActionCfg, JointEffortActionCfg, or RelativeJointPositionActionCfg
Reuse XML actuators	XmlActuatorCfg on EntityArticulationInfoCfg
Stable implicit PD in MuJoCo	BuiltinPositionActuatorCfg, BuiltinPdActuatorCfg, or BuiltinDcMotorActuatorCfg
Explicit torque/PD/DC dynamics	IdealPdActuatorCfg, DcMotorActuatorCfg, LearnedMlpActuatorCfg
Robot target lookup	SceneEntityCfg with joint/body/geom/site/actuator names
Standard observations	mjlab.envs.mdp.observations functions
Standard penalties/rewards	mjlab.envs.mdp.rewards plus task-family rewards
Episode stop logic	mjlab.envs.mdp.terminations and task-family terminations
Reset or perturb states	EventTermCfg with event functions

Boundary routing

• Manager dictionary shape and lifecycle: environment-configuration.
• Entity/spec attachment and scene assembly: scene-simulation-assets.
• Sensor, terrain, and domain-randomization details: perception-terrain-randomization.
• Task registry and CLI training/play execution: training-evaluation-cli.