Robotics: ENACT raises nearly 27 million euros to develop the sense of touch

The Zurich startup is raising nearly 27 million euros in pre-seed funding to develop robots capable of better perceiving forces and adjusting their movements when in contact with objects. His challenge consists of designing together the mechanics, the data and the artificial intelligence models necessary for manipulation. In France, Yann LeCun and Arthur Mensch are participating in the financing.

Handling a cable, using a tool, carrying out assembly: these actions appear in the Enact Intelligence data collection program. What they have in common is that they ask the robot to interact with matter, to accompany a movement and to adapt to what happens during the action. For the Swiss company, part of the answer lies in the information that the machine can collect about its own movements and the forces it encounters.

Enact Intelligence thus intends to develop robotics capable of learning fine manipulation by combining AI models, sensors and actuators, the components that produce movement. The question relates as much to the design of the robot as to that of its learning: what information must be given to it so that it correctly adjusts its gesture?

The startup describes its approach as a proprioceptive system associated with models capable of exploiting force information. Proprioception informs the robot about its own mechanical state; the perception of efforts should help him to adapt his behavior during manipulation.

The “sense of touch” here covers this ability to take into account physical interaction. Enact emphasizes forces and proprioception, without detailing its entire sensor architecture, so it would be premature to imagine artificial skin reproducing all the sensations of a human hand.

The objective is to enable the learning of manipulation skills that can be used on different tasks and in different environments. An ambition that we discovered in the analysis of the startup's recruitment: future scientists must work on force-sensitive models, organize training data and evaluate behavior on real robots. Their program includes in particular the effectiveness of learning and robustness when moving to real conditions.

The sensors collect the information necessary to estimate the state of the robot and its interactions.

The actuators produce the movements whose effects the system must control.

Human demonstrations provide examples of gestures to learn.

AI models are trained and evaluated to transform this information into robotic behaviors.

Enact seeks to develop these links together. The gain obtained on an industrial task remains to be established by measured results.

Mechanics enters the learning loop

Machine-translated from French