[Question] How to assign the root position of a actor in manager based rl

[Zyl-000]

Question

Hi, I am using the manager based rl env to train my rl policy, I want to assign the root positions of the actors after each steps, such as set_root_state in post_physics_steps in isaacgymenvs. How to do this in the manager based rl framework, thank you

[RandomOakForest]

Thanks for posting this. In Isaac Lab, the post_physics_step has been moved to the framework in the base class. For this and other related changes, see this section of the docs.

[Zyl-000]

Thanks for the reply. I am using the manager based rl env, can I modify the framework? I want to call this function after (or before) every step, how can I achieve this?

[RandomOakForest]

Thanks for following up. I'll move this post to our Discussions section. Here is a summary of a what you may try.

In Isaac Lab's manager-based RL environment, you can modify the workflow by overriding specific methods in your custom environment class. The framework allows injecting custom logic before or after each step by leveraging its structured workflow. Here's how to achieve this:

1. Override Core Methods

The base ManagerBasedRLEnv class provides hooks for custom logic:

from isaaclab.envs import ManagerBasedRLEnv

class CustomRLEnv(ManagerBasedRLEnv):
    def _pre_physics_step(self, actions: torch.Tensor) -> None:
        """Called before physics simulation steps."""
        # Custom logic before physics steps
        super()._pre_physics_step(actions)

    def _post_physics_step(self) -> None:
        """Called after physics simulation steps."""
        super()._post_physics_step()
        # Custom logic after physics steps (e.g., logging, additional computations)

2. Key Workflow Points

Method	Purpose	Override For
_pre_physics_step	Processes actions from the policy before simulation steps.	Pre-step logic (e.g., action scaling)
_apply_action	Applies processed actions to the simulation.	Direct actuator control
_post_physics_step	Computes terminations, rewards, resets, and observations.	Post-step logic (e.g., custom metrics)

3. Decimation Handling

For environments using decimation (multiple simulation steps per RL step):

def step(self, action: torch.Tensor) -> tuple:
    # Custom pre-step logic
    for _ in range(self.cfg.decimation):
        self._apply_action()  # Called before each simulation substep
        self.sim.step()
    # Custom post-step logic
    return super().step(action)

4. Full Workflow Customization

To completely modify the step sequence, override the step() method:

def step(self, actions: torch.Tensor) -> tuple:
    # Custom pre-processing
    self.action_manager.process_action(actions)
    
    # Physics stepping loop
    for _ in range(self.cfg.decimation):
        self.scene.write_data_to_sim()
        self.sim.step()
        self.scene.update(self.physics_dt)
    
    # Post-step computations
    self.episode_length_buf += 1
    observations = self._get_observations()
    rewards = self._get_rewards()
    dones = self._get_dones()
    
    # Custom post-processing
    return observations, rewards, dones, {}

Key Changes from IsaacGymEnvs

IsaacGymEnvs	Isaac Lab Equivalent	Customization Point
post_physics_step	Split into _get_dones/_get_rewards	Override individual components
pre_physics_step	_pre_physics_step + _apply_action	Separate action processing/application

Example Use Case

class MonitoringEnv(ManagerBasedRLEnv):
    def _post_physics_step(self):
        super()._post_physics_step()
        # Add custom monitoring after every step
        self._log_custom_metrics()
        
    def _log_custom_metrics(self):
        cart_pos = self.scene["robot"].data.joint_pos[:, 0]
        print(f"Average cart position: {cart_pos.mean().item():.2f}")

This approach maintains compatibility with Isaac Lab's manager system while allowing injection of custom logic at precise points in the simulation loop.