Top 10 novarctech.com Competitors

bostondynamics.com

NASA JPL added two Spots to their fleet of heterogeneous autonomous robots tackling the DARPA Subterranean Challenge.

mujin-corp.com

Mujin builds intelligent robotics software for factories and warehouses. MujinOS enables no-code deployment of palletizing, picking, and fleet automation.

dobot.cn

越疆机器人专注于协作机器人的研发与应用，是全球先进的智能机器人研发制造商。拥有cr、crs、mg400、m1pro、nova、magician 六大系列十余款协作机器人，覆盖 3c、汽车、半导体、化工、医疗、金属加工、食品饮料、新零售、教育等多个行业。服务热线：400-800-7266。

technohorizon.co.jp

テクノホライゾン株式会社は、映像＆ITとロボティクスで豊かな社会を実現するグローバルカンパニーです。

sereact.ai

Start benefiting from the advantages of autonomous pick and place now. Contact us to discuss your specific use case, schedule a live demo, or a free performance validation.

forcen.tech

World’s first and only force/torque sensing systems uniquely designed for complex, unstructured robotics across surgical, logistics, humanoid, space, & frontier applications.

synthiam.com

Easily program and make robots using technologies from industry experts. Built for DIY builders, educators, and enterprise teams. Robot programming software to make a robot. Computer vision, robot navigation and artificial intelligence.

primerobotics.com

For valuable resources on the robotics industry, explore our informative materials and stay abreast of the latest industry trends.

odriverobotics.com

ODrive - High performance motor control for robotics.

umi-gripper.github.io

We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to deployable robot policies. UMI employs hand-held grippers coupled with careful interface design to enable portable, low-cost, and information-rich data collection for challenging bimanual and dynamic manipulation demonstrations. To facilitate deployable policy learning, UMI incorporates a carefully designed policy interface with inference-time latency matching and a relative-trajectory action representation. The resulting learned policies are hardware-agnostic and deployable across multiple robot platforms. Equipped with these features, UMI framework unlocks new robot manipulation capabilities, allowing zero-shot generalizable dynamic, bimanual, precise, and long-horizon behaviors, by only changing the training data for each task. We demonstrate UMI’s versatility and efficacy with comprehensive real-world experiments, where policies learned via UMI zero-shot generalize to novel environments and objects when trained on diverse human demonstrations.