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New robotic control software avoids jamming their joints

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Why This Matters

The EPFL team's new robotic control software, Kinematic Intelligence, enables seamless transfer of learned skills between different robot models, reducing setup time and increasing flexibility in robotics applications. This advancement is significant for the industry as it simplifies robot upgrades and customization, making automation more adaptable and cost-effective for consumers and manufacturers alike.

Key Takeaways

Switching from one smartphone to another is mostly a smooth procedure. You log into your accounts and your apps, preferences, and contacts should sync to the new hardware. But in the world of robotics, swapping an old robotic arm for a newer model has meant setting everything up from scratch.

To fix that, a team of researchers at the Swiss École Polytechnique Fédérale de Lausanne (EPFL) has developed what they call Kinematic Intelligence, a framework that makes switching robots work more like switching smartphones. They describe their system in a recent Science Robotics paper.

Demonstrating skills

For years, roboticists have been working on getting robots to learn from demonstration—teaching them new skills by showing them what to do, rather than writing lines of code. The idea is to remotely control or physically guide the robot’s arm to teach it a task like wiping a table, stacking boxes, or welding a car component. The problem is that most of these taught skills end up tied to the specific robot the training was done with.

But robotics is advancing quickly. “The robots have different designs, and nowadays there are new designs being proposed—that brings its own set of challenges,” said Sthithpragya Gupta, a roboticist at EPFL and lead author of the study. If a new robot has slightly longer links, a different joint orientation, or a more complex configuration, that learned behavior instantly breaks and the new robot will likely flail, freeze, or crash if attempting it.

“With new designs come different capabilities and constraints,” said Durgesh Haribhau Salunkhe, an EPFL roboticist and co-author of the study. “The problem is to adapt to these constraints and capabilities—to faithfully replicate the actions demonstrated by a human.” Today, making the leap from one robot body to another usually means starting from scratch and retraining the whole system.