srb agent teleop — Teleoperate Agent
The srb agent teleop command allows you to manually control robots in the environment using various input devices. This is useful for testing the environment and observing the behavior of the agent under specific control inputs.
Usage
srb agent teleop --env ENV_ID [options]
Options | Shared Agent Options
| Option | Description | Default |
|---|---|---|
--teleop_device [DEV …] | Interface (keyboard, spacemouse, ros, gamepad, haptic) | env default, else [keyboard] |
--pos_sensitivity VAL | Translation sensitivity | 1.0 |
--rot_sensitivity VAL | Rotation sensitivity (rad/s) | π (≈3.14) |
--invert_controls | Invert control schema | True |
| Teleoperation via Policy | ||
--algo ALGO | Algorithm of the policy (RL or IL) | |
--model MODEL | Path to the policy checkpoint | |
--obs {state,visual} | Observation modality of the policy: selects the agent-config variant and the log tree searched for its latest checkpoint | state |
Teleoperation Modes
The teleop subcommand supports 2 modes of operation based on the environment action space:
Direct Teleoperation
Direct teleoperation is available for environments with action spaces that can be mapped into high-level control commands, such as the desired end-effector pose or acceleration of a mobile robot.
Examples
Teleoperate a robot in the _manipulation template environment:
- Use the
ur10manipulator
srb agent teleop -e _manipulation env.robot=ur10
Teleoperate a robot in the _orbital template environment:
- Use the
spacemouseinput teleoperation device
srb agent teleop -e _orbital --teleop_device spacemouse
Teleoperation via Policy
Some action spaces do not support direct teleoperation due to their complexity or dimensionality, such as joint-space control of a humanoid robot. In such cases, you can still teleoperate the agent by providing a trained policy (both RL and imitation-learning policies are supported; the algorithm can be any --algo accepted by srb agent eval). In this case, command-like observations are driven by the teleoperation device, and the policy generates the corresponding actions.
Examples
Reference: Reinforcement Learning Workflow
Reference:srb agent train— Train Agent
First, you need to train an RL agent using the srb agent train command. Let’s demonstrate this with the locomotion_velocity_tracking environment using the dreamer algorithm:
srb agent train --headless --algo dreamer -e locomotion_velocity_tracking env.num_envs=256
After you have successfully trained a policy, you can teleoperate the agent:
srb agent teleop --algo dreamer -e locomotion_velocity_tracking
By default, the policy will be loaded from the latest checkpoint. However, you can specify a specific checkpoint using the --model option.
srb agent teleop --algo dreamer -e locomotion_velocity_tracking --model space_robotics_bench/logs/locomotion_velocity_tracking/dreamer/...
Public Demo Launch
The excavation_demo task is tuned for public events: one environment, manual reset, long horizon, all PRD-stipulated HUD lines visible, and the high-quality RTX preset selected automatically. Operators can launch it with a single command:
srb agent teleop --env excavation_demo
Add --hide_ui to drop the Isaac Sim UI for a fullscreen presentation station:
srb agent teleop --env excavation_demo --hide_ui
The default teleop devices (spacemouse, haptic, keyboard) come from the task config, so --teleop_device is optional. Hydra overrides such as env.scenery=lunalab or env.demo_regolith_quality=visual remain available for venue and visual-tier choices.