Eka Robotics Secures $150M Series A at $1.45B Valuation
Eka Robotics hits $1.45 billion valuation with $150 million Series A
Eka Robotics, founded by MIT’s Agrawal and Haarnoja, uses vision-force-action models for robotic control. The system employs physics simulators and reinforcement learning, achieving 95% success in picking 1,500 objects per hour.
Source: Robotics — Web news discovery · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
valuation
1,450,000,000
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Eka Robotics hits $1.45 billion valuation with $150 million Series A
Open source S1funding_round_amount
150,000,000
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Eka Robotics hits $1.45 billion valuation with $150 million Series A
Open source S1Reported success rate
95%
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r 5 million duty cycles, with a goal of reaching the 20 million cycles standard for industrial arms. Eka says the robot picks about 1,500 objects weighing up to 1 kilogram per hour with a success rate above 95%. Agrawal said vision is not the primary way to solve the “last millimeter problem” of precise manipulation. Raghu Raguram, a general partner at a16z, said the robot is already exceeding superhuman performance
Open source S4object_picked_per_hour
≈1,500
View original evidence
r 5 million duty cycles, with a goal of reaching the 20 million cycles standard for industrial arms. Eka says the robot picks about 1,500 objects weighing up to 1 kilogram per hour with a success rate above 95%. Agrawal said vision is not the primary way to solve the “last millimeter problem” of precise manipulation. Raghu Raguram, a general partner at a16z, said the robot is already exceeding superhuman performance
Open source S4gripper_cost
1,000
View original evidence
ensing instead of human demonstrations Eka has built what it calls a vision-force-action model, which links what a robot sees with the forces it feels to decide how to move, rather than relying mainly on vision and language. The system is trained with a physics simulator and large-scale reinforcement learning instead of videos of human demonstrations. Its force-sensitive gripper costs less than $1,000 and is rated fo
Open source S3robot_weight_capacity
≤1
View original evidence
r 5 million duty cycles, with a goal of reaching the 20 million cycles standard for industrial arms. Eka says the robot picks about 1,500 objects weighing up to 1 kilogram per hour with a success rate above 95%. Agrawal said vision is not the primary way to solve the “last millimeter problem” of precise manipulation. Raghu Raguram, a general partner at a16z, said the robot is already exceeding superhuman performance
Open source S4
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
Eka Robotics hits $1.45 billion valuation with $150 million Series A
Open source S1
r 5 million duty cycles, with a goal of reaching the 20 million cycles standard for industrial arms. Eka says the robot picks about 1,500 objects weighing up to 1 kilogram per hour with a success rate above 95%. Agrawal said vision is not the primary way to solve the “last millimeter problem” of precise manipulation. Raghu Raguram, a general partner at a16z, said the robot is already exceeding superhuman performance
Open source S4
ensing instead of human demonstrations Eka has built what it calls a vision-force-action model, which links what a robot sees with the forces it feels to decide how to move, rather than relying mainly on vision and language. The system is trained with a physics simulator and large-scale reinforcement learning instead of videos of human demonstrations. Its force-sensitive gripper costs less than $1,000 and is rated fo
Open source S3
Source：Robotics — Web news discovery · cryptobriefing.com