Bear Robotics Launches KINO-2 Through Engineering Collaboration with NVIDIA
Bear Robotics Launches KINO-2 Through Engineering Collaboration with NVIDIA
Bear Robotics has launched KINO-2, a robot-learning platform that transforms task demonstrations into deployable manipulation skills for its KR1 humanoid robot. The platform integrates with NVIDIA Isaac OS and leverages foundation models to enhance inference speed and operational efficiency in commercial environments.
Bear Robotics introduces KINO-2, a robot-learning platform that enables task demonstrations to be converted into deployable manipulation skills on its KR1 humanoid robot. The platform integrates with NVIDIA Isaac OS and leverages foundation models to improve inference speed and operational efficiency in commercial environments.
Source: Robotics — Robot learning 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
robots_deployed
>16,000 robots
View original evidence
Bear has deployed over 16,000 robots, mostly service robots, at over 5,000 customer locations. Through those deployments, Bear has built navigation, fleet coordination and operational infrastructure, along with practical knowledge of how robots work alongside people in commercial environments. Many customer workflows require both movement and manipulation. A crate must be packed before it can be transported, a machi
Open source S3customer_locations
>5,000
View original evidence
Bear has deployed over 16,000 robots, mostly service robots, at over 5,000 customer locations. Through those deployments, Bear has built navigation, fleet coordination and operational infrastructure, along with practical knowledge of how robots work alongside people in commercial environments. Many customer workflows require both movement and manipulation. A crate must be packed before it can be transported, a machi
Open source S3inference_time_initial
≈500
View original evidence
ls, including NVIDIA Isaac GR00T , into a broader system spanning navigation and SLAM, fleet management and workflow coordination. Using the NVIDIA Isaac OS runtime alongside NVIDIA TensorRT and model quantization, Bear reduced GR00T inference time from approximately 500 milliseconds in its initial unoptimized deployment to around 60 milliseconds—roughly an 8× speedup in internal measurements. Bear’s in-house model a
Open source S11inference_time_optimized
≈60
View original evidence
ls, including NVIDIA Isaac GR00T , into a broader system spanning navigation and SLAM, fleet management and workflow coordination. Using the NVIDIA Isaac OS runtime alongside NVIDIA TensorRT and model quantization, Bear reduced GR00T inference time from approximately 500 milliseconds in its initial unoptimized deployment to around 60 milliseconds—roughly an 8× speedup in internal measurements. Bear’s in-house model a
Open source S11inference_speedup_in_house_model
4.2
View original evidence
lso achieved a 4.2× inference speedup, reducing inference time from 72.3 to 17.2 milliseconds. KINO-2 launches at NVIDIA GTC Berlin Bear is launching KINO-2 at NVIDIA GTC Berlin, alongside NVIDIA’s introduction of the first public beta of Isaac OS and general availability of Holoscan SDK (HSDK) 5.0, part of the Isaac OS runtime. At NVIDIA’s invitation, Bear is demonstrating KINO-2 powering KR1 at the NVIDIA booth. Wo
Open source S12inference_speedup_gr00t
8
View original evidence
ls, including NVIDIA Isaac GR00T , into a broader system spanning navigation and SLAM, fleet management and workflow coordination. Using the NVIDIA Isaac OS runtime alongside NVIDIA TensorRT and model quantization, Bear reduced GR00T inference time from approximately 500 milliseconds in its initial unoptimized deployment to around 60 milliseconds—roughly an 8× speedup in internal measurements. Bear’s in-house model a
Open source S11
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
Bear has deployed over 16,000 robots, mostly service robots, at over 5,000 customer locations. Through those deployments, Bear has built navigation, fleet coordination and operational infrastructure, along with practical knowledge of how robots work alongside people in commercial environments. Many customer workflows require both movement and manipulation. A crate must be packed before it can be transported, a machi
Open source S3
ls, including NVIDIA Isaac GR00T , into a broader system spanning navigation and SLAM, fleet management and workflow coordination. Using the NVIDIA Isaac OS runtime alongside NVIDIA TensorRT and model quantization, Bear reduced GR00T inference time from approximately 500 milliseconds in its initial unoptimized deployment to around 60 milliseconds—roughly an 8× speedup in internal measurements. Bear’s in-house model a
Open source S11
lso achieved a 4.2× inference speedup, reducing inference time from 72.3 to 17.2 milliseconds. KINO-2 launches at NVIDIA GTC Berlin Bear is launching KINO-2 at NVIDIA GTC Berlin, alongside NVIDIA’s introduction of the first public beta of Isaac OS and general availability of Holoscan SDK (HSDK) 5.0, part of the Isaac OS runtime. At NVIDIA’s invitation, Bear is demonstrating KINO-2 powering KR1 at the NVIDIA booth. Wo
Open source S12
Source：Robotics — Robot learning news discovery · cityam.com