UMI on Legs is an innovative framework designed to integrate real-world human demonstrations with simulation-trained whole-body controllers. The objective? To create a scalable approach for enabling manipulation skills on robotic dogs equipped with arms. The beauty of this framework lies in its plug-and-play nature, allowing you to use your existing visuomotor policies on quadrupedal platforms, effectively making your manipulation capabilities mobile!

Getting Started with UMI on Legs

If you are eager to dive right into running some commands while skimming the related paper, you can find everything you need to get started here. This includes downloading data, loading checkpoints, and executing the Whole-body Controller (WBC).

Table of Contents

• Getting Started
• Setup
• Checkpoint Data
• Rollout
• Evaluation
• Curves
• Universal Manipulation Interface
• Data Collection
• Hardware Guide
• Preprocessing
• Manipulation-Centric Whole-body Controller
• Train
• Robustifying Sim2Real
• Extending
  • More Robots
  • More Manipulation Trajectories
• Real World Deployment
• Reflections on Hardware Choices
• Bill of Materials
• ARX5 Robot Arm SDK
• iPhone Odometry
• 3D Printing Guide
• Assembly Guide
• Unitree Robots Network Setup
• Deploy WBC on Real Robots
• Visualizations

How UMI on Legs Works: An Analogy

Imagine your robotic dog as a talented dancer on a performance stage. The magic happens when this dancer learns new moves (manipulation skills) from observing a human (real-world demonstrations). The UMI framework becomes the dance coach, combining the intricate steps of a ballet routine (simulation-trained whole-body controllers) with an eagerness to learn new styles from dancers of different backgrounds (real-world manipulation). Just like dancers can adapt their techniques, UMI allows the robotic dog to assimilate those skills into its repertoire, performing beautifully on its four legs!

Troubleshooting Ideas

Encounters with hurdles are part of any innovative journey. Here are some ideas to get you back on track:

• If you experience difficulties in setup, ensure that all necessary dependencies are properly installed — sometimes a missing library can derail your plans.
• Check your connections — if your robotic dog isn’t responding, a loose cable might be the culprit.
• If things aren’t working as expected after rolling out the WBC, review the configuration parameters to ensure that they were set correctly.
• If you find yourself stuck with software errors, reach out to fellow developers or forums for insights.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.