Eka Robotics raises $150M for force-sensitive robot manipulation
Eka Robotics closed a $150 million Series A at a $1.45 billion post-money valuation, led by a16z and F-Prime. Its Vision-Force-Action model combines vision with force-sensor feedback, while a force-sensitive gripper and reinforcement learning in physics simulation support object manipulation. Eka claims its system can pick 1,500 objects weighing up to 1 kg per hour with greater than 95% reliability, though The Next Web did not verify those figures and unnamed industrial pilots remain underway.
The funding backs a substantive physical-AI approach that combines touch, vision and reinforcement learning, but its reported throughput, reliability and production-line readiness have not been independently established.
Robots still aren’t that good at picking things up, despite billions of dollars of investment and endless videos fed into deep learning models.
That may be about to change, thanks to a manipulation architecture built by Eka Robotics, which encompasses a force-sensitive gripper and a new kind of deep-learning model. Founded by MIT professor Pulkit Agrawal and former Google DeepMind researcher Tuomas Haarnoja, the startup just closed an $150 million Series A round that values it at $1.45 billion post-money, led by venture funds a16z and F-Prime.
“They are already exceeding superhuman performance for certain tasks, and so we think over the next few months there will be superhuman performance in many dexterous manipulation tasks,” Raghu Raghuram, the a16z general partner who co-led the fundraising round, told TechCrunch.
In an era of never-ending robotic demonstrations, Eka’s capabilities are shocking veteran robot builders.
Kris Hauser, a professor at the University of Illinois Urbana-Champaign, said it had a “wow factor” compared to other robot demos he saw recently. The Eka robot “was able to adapt to a large number of different objects without retraining,” Hauser said, adding that it also had an aspect of “humanness, creepy humanness that it would try over and over again if it failed the first time.”
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