According to EPFL engineers, enabling skills to transfer directly between robots could make these systems more sustainable and cost-efficient. Their concept of “kinematic intelligence” takes a human-demonstrated task, mathematically converts it into a general movement strategy, and then adapts it so that different robots can perform it based on their physical design.

“This work addresses a long-standing challenge in robotics: how to transfer a learned skill across robots with different mechanical structures, while guaranteeing safe and predictable behavior,” says Aude Billard, Ph.D., the head of the Learning Algorithms and Systems Laboratory (LASA). “This approach could significantly reduce the time and expertise needed to deploy robots in real-world settings.”

To build their framework, Billard and her colleagues first took human-demonstrated object manipulation tasks, such as placing, pushing and throwing, and recorded them using motion-capture technology. Then, they mathematically converted these recorded tasks into general movement strategies.

They also developed a systematic classification of the physical limits of different robot designs, including how far their joints can move and which positions they must avoid to remain stable. The framework then uses this classification to automatically tailor the general movement strategies to different robot bodies, ensuring they can carry out tasks safely within their mechanical limits.

In an assembly line experiment, a human demonstrated a task by pushing a wooden block off a conveyor belt onto a workbench, placing it on a table and finally throwing it into a basket. By using Kinematic Intelligence, three different commercial robots were able to reproduce this same sequence safely and reliably. Each robot handled different steps of the task, and Billard says the system performed successfully even when the step allocation was changed.

“[We] aim to extend the framework to settings such as human-robot collaboration and natural language-based interaction,” notes Billard. “For example, Kinematic Intelligence could allow a person to instruct a robot with simple commands at home, with no need for technical programming.

“The approach is also relevant for emerging robotic platforms, where rapid hardware evolution means that today’s machines may soon be replaced by newer versions,” claims Billard. “Enabling seamless transfer of skills across such platforms could play a key role in making them practical and scalable. Our goal is to remove the need for technical expertise, while still ensuring safe and reliable operation.”