Before learning how to train a reinforcement learning policy for robot control, we first need to understand which types of tasks are suitable for RL.
For systems that are easy to model and predict, model-based methods are usually more appropriate because their behavior is deterministic, interpretable, and easier to control. For example, inverse kinematics can directly compute the joint configuration required to reach a target pose.
The strength of RL appears when the problem is too complex to model explicitly (ie. high dimensional robot control, unstructured env etc). Instead of deriving an exact controller from equations, RL learns an approximate control strategy through billions interaction with the environment.
RL is particularly suitable when:
-
The control strategy is difficult to model explicitly
Methods such as IK can calculate joint positions for a desired pose, but they do not directly determine how the robot should handle dynamics, momentum, contact forces, or changing contact conditions. When the correct control law is difficult to derive manually, RL can learn it from experience. -
The task involves uncertainty and disturbances
The robot may need to operate under sensor noise, model mismatch, external forces, varying friction, or unexpected environmental changes. RL can be trained across these variations to learn a robust closed-loop response. -
The task requires high-dimensional coordination
Many joints or subsystems may need to work together, and there may be multiple valid ways to complete the task. When success is easier to evaluate than the exact motion is to program, we can define rewards for objectives such as stability, efficiency, speed, or smoothness and allow RL to discover an effective strategy.
It’s highly recommand that you understand the basic principal of RL. huggingface has good article about it.
To train an effective policy for a robot, we must first define the robot accurately, including correct robot urdf/xml, actuator model, collision, and scene.
Then we need to clearly define the task logic through its core components:
1. Rewards
The reward function defines what is good or bad and how strongly each behavior matters through its weight. For walking, it may reward velocity tracking and balance while penalizing slipping, excessive torque, jerky motion, or falling. Parameters such as standard deviation control how much error is tolerated. The policy then learns the joint actions that maximize long-term reward.
2. Observation
The observation represents the robot’s estimated current state, obtained from sensors and motor encoders. For walking, it may include IMU data, joint positions and velocities, desired velocity, and the previous action.
3. Termination Condition
An episode is one complete training attempt, from the initial state until a termination condition is reached. Termination conditions end the episode when the robot enters an unrecoverable or invalid state, such as falling or exceeding a safe tilt angle. This prevents the policy from collecting noisy, meaningless experience after failure and allows training to restart from a useful state.
4. Action
The action is the control command produced by the policy at each step. For robot, it is commonly joint positions, velocities, or torques that are sent to the robot controller.
5. Events
Events define how the environment changes during training, often through domain randomization. They may vary friction, mass, sensor noise, commands, or apply external pushes, exposing the policy to diverse conditions and improving robustness.
But how should these terms be defined, and what configuration best represents the task?
As a beginner, the best way to start is by starting an existing example. There’re lots of open sourced codebase that defined a few classic reinforcement learning task well. Let’s take IsaacLab as an example:
Open the task folder under managerbased:
tasks/
├── classic/ # Classic robotics tasks
├── drone_arl/ # Multirotor and aerial reinforcement learning tasks
├── locomanipulation/ # Tasks combining locomotion and manipulation
├── locomotion/ # Legged and humanoid locomotion tasks
├── manipulation/ # Robot manipulation and object-interaction tasks
├── navigation/ # Robot navigation tasks
└── __init__.py # Task registration and package initialization
These examples cover different robot configurations, including drones, manipulators, mobile robots, and legged robots. Select the task that is closest to your own problem, understand how its observations, actions, rewards, terminations, and events are defined, and then modify them gradually.
For example, suppose we want to train a robot arm to reach an apple placed at a random position within . We can begin with an existing reaching task and adapt its observations, target generation, reward function, and environment randomization to match this objective.
reach_env_cfg.py defines the reaching task at a high level by configuring its scene, observations, actions, rewards, terminations, commands, and events. And the detailed functions are defined in the folder mdp/.
The observation terms are crucial for the policy to do state estimation related to the task. Joint pose and velocity tells the policy implicitly where’s the end effector position and robot dynamics, pose command defines how far away the end effector is to the apple, and previous action implicitly tells a dynamic of how the action is transiting the robot’s state.
class ObservationsCfg:
"""Observation specifications for the MDP."""
# observation terms (order preserved)
joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01))
joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-0.01, n_max=0.01))
pose_command = ObsTerm(func=mdp.generated_commands, params={"command_name": "ee_pose"})
actions = ObsTerm(func=mdp.last_action)
As introduced earlier, reward terms evaluate how well the robot is performing relative to the task objective. In this reaching task, the main rewards measure the position and orientation error between the robot’s end effector and the apple, with higher rewards given as the end effector gets closer to the target. Action penalty terms discourage abrupt or excessive changes in the robot’s commands, helping produce smoother motion.
class RewardsCfg:
"""Reward terms for the MDP."""
# task terms
end_effector_position_tracking = RewTerm(
func=mdp.position_command_error,
weight=-0.2,
params={"asset_cfg": SceneEntityCfg("robot", body_names=MISSING), "command_name": "ee_pose"},
)
end_effector_position_tracking_fine_grained = RewTerm(
func=mdp.position_command_error_tanh,
weight=0.1,
params={"asset_cfg": SceneEntityCfg("robot", body_names=MISSING), "std": 0.1, "command_name": "ee_pose"},
)
end_effector_orientation_tracking = RewTerm(
func=mdp.orientation_command_error,
weight=-0.1,
params={"asset_cfg": SceneEntityCfg("robot", body_names=MISSING), "command_name": "ee_pose"},
)
# action penalty
action_rate = RewTerm(func=mdp.action_rate_l2, weight=-0.0001)
joint_vel = RewTerm(
func=mdp.joint_vel_l2,
weight=-0.0001,
params={"asset_cfg": SceneEntityCfg("robot")},
)
The termination condition defines when each training episode ends. In this reaching task, the episode terminates only when the maximum time limit is reached.
class TerminationsCfg:
"""Termination terms for the MDP."""
time_out = DoneTerm(func=mdp.time_out, time_out=True)
Events define changes or perturbations applied during simulation. In this example, the robot’s joints are reset at the start of each episode with randomized positions scaled between 0.5 and 1.5, while joint velocities are set to zero.
class EventCfg:
"""Configuration for events."""
reset_robot_joints = EventTerm(
func=mdp.reset_joints_by_scale,
mode="reset",
params={
"position_range": (0.5, 1.5),
"velocity_range": (0.0, 0.0),
},
)