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Eka Robotics Inc. has completed a $150 million Series A funding round. On October 20, 2026, a16z announced it had led the round, betting on the company's approach to solving the challenge of robotic dexterous manipulation. Eka builds Vision-Force-Action models that combine visual perception with robotic hand force feedback, and trains manipulation policies with reinforcement learning in physics simulation, enabling robots to gain information from contact itself and to correct in real time when slipping occurs or insertion is obstructed. The founding team includes Pulkit Agrawal of MIT CSAIL and Tuomas Haarnoja, co-proposer of Soft Actor-Critic. A report the next day said the round was led by Andreessen Horowitz and Fidelity's F-Prime, valuing the company at $1.45 billion; the force sensors in its custom grippers can sense grip strength, allowing the robot to adjust its grasp mid-way when an object slips.
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Eka Robotics raises $150 million at a $14.5 billion valuation to build robotic hands with a sense of touchDevelopments
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- Source:a16z:News
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Oct 21
SiliconANGLE
Eka Robotics raises $150 million at a $14.5 billion valuation to build robotic hands with a sense of touch
Eka Robotics has completed a $150 million Series A funding round at a $14.5 billion valuation, led by Andreessen Horowitz and Fidelity's F-Prime. Its vision-force-action model uses force sensors in custom grippers to sense grip strength, allowing it to adjust its hold mid-grasp when an object slips. The robot can pick up about 1,500 objects per hour that are outside its training data, compared with a human baseline of about 600. The gripper costs less than $1,000 and is currently rated for 5 million work cycles; the company aims to raise that to 20 million, the standard for industrial robotic arms.
Oct 20
a16z:News
a16z Leads Eka Robotics Series A Funding
a16z announced it has led the Series A funding round of Eka Robotics, betting on its approach to solving the challenge of robotic dexterous manipulation. Eka builds a Vision-Force-Action model that combines visual perception with robotic hand force feedback, and trains manipulation policies with reinforcement learning in physical simulation, enabling robots to obtain information from contact itself and correct in real time when slipping or insertion is obstructed. The founding team includes Pulkit Agrawal of MIT CSAIL and Tuomas Haarnoja, co-proposer of Soft Actor-Critic.