RLBotics: Reinforcement Learning Skills
WARNING
RLBotics is not yet released.
This module is under active development and will be available in a future Telekinesis SDK release.
INFO
RLBotics is a module in the Telekinesis SDK for reinforcement learning (RL) skills in robotics.
It provides tools to train RL policies, simulate them, and deploy them sim-to-sim or sim-to-real, so you can run learned behaviors in simulation or on real hardware with a single pipeline.
Install Telekinesis Skill Library
Create an API key, install
telekinesis-ai and module specific installations and dependencies in a Python environment. Go to the Quickstart
Pick a starter and run your first Skill end-to-end - 2D / 3D vision, webcam capture, robot motion, or a full vision-to-robot pipeline, all visualized in Rerun.
When to Use RLBotics?
Use RLBotics for robotics applications that require learned control and behaviors, such as:
- Training RL policies for locomotion, manipulation, or control in simulation
- Simulating and validating policies before deployment
- Deploying policies sim-to-sim (e.g., from one simulator to another) or sim-to-real (from simulation to real robots)
- Integrating learned control into Physical AI pipelines alongside perception and planning
What Does RLBotics Provide?
RLBotics includes a collection of modular skills for:
- Training RL policies in simulation with support for common algorithms and backends
- Running and debugging policies in simulation (stress-testing, tuning, validation)
- Sim-to-sim deployment: running the same policy across different simulators
- Sim-to-real deployment: transferring policies from simulation to real robots with a unified interface