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BDX-R: A Journey in Bipedal Robotic Locomotion

This repository chronicles the development of BDX-R, a personal endeavor to create a bipedal robot inspired by Disney's BDX droids. The primary goal is to achieve stable walking and successfully bridge the simulation-to-reality (Sim2Real) gap using reinforcement learning.

BDX-R in Isaac Lab Simulation BDX-R Physical Prototype

🎯 Current Focus: Walking and Sim2Real

The project is currently in its initial phase, with the core focus on mastering bipedal locomotion. The immediate objectives are:

  • Achieve Stable Walking: Train a robust walking policy using reinforcement learning.
  • Cross the Sim2Real Gap: Successfully transfer the policy trained in a simulated environment to the physical robot.

At this stage, the project is concentrated on the fundamental mechanics of the body's movement. Expressiveness and the integration of a head are future goals to be explored after mastering stable locomotion.


🛠️ Hardware

The BDX-R is built with a focus on high-performance components that are accessible to the robotics community. The entire build is being developed with a target budget under $3,000.

  • Robstride Motors: These motors provide the necessary torque and precision for dynamic and controlled leg movements.
  • NVIDIA Jetson Orin Nano: Serving as the onboard computer, the Jetson Orin Nano has the computational power required to run the trained RL policy in real-time.

🤖 Software and Training: Reinforcement Learning with Isaac Lab

The robot's ability to walk is being developed through reinforcement learning within the NVIDIA Isaac Lab simulation environment.

A policy is trained in this virtual space, allowing the BDX-R to learn and adapt its movements to maintain balance and achieve forward motion. This process is critical for developing a robust control system before deploying it on the physical hardware.


🚀 Installation

To install the necessary packages for this project, after cloning the repo, run the following command:

python -m pip install -e source/BDXR

🙏 Community and Acknowledgements

This project is a personal learning journey and would not have been possible without the guidance and inspiration from the wider robotics community. A special thank you to:

Their expertise and willingness to share knowledge have been invaluable.

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Isaaclab Reinforcement Learning for the BDX-R Robot

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