{"id":4605,"date":"2026-06-30T18:42:52","date_gmt":"2026-06-30T18:42:52","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2026\/06\/30\/how-jaiveer-singh-is-helping-robots-and-developers-move-faster\/"},"modified":"2026-06-30T18:42:52","modified_gmt":"2026-06-30T18:42:52","slug":"how-jaiveer-singh-is-helping-robots-and-developers-move-faster","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2026\/06\/30\/how-jaiveer-singh-is-helping-robots-and-developers-move-faster\/","title":{"rendered":"How Jaiveer Singh Is Helping Robots \u2014 and Developers \u2014 Move Faster"},"content":{"rendered":"
\n

When Jaiveer Singh talks about robots, he doesn\u2019t begin with spectacle. He begins with infrastructure: the boards inside machines, the software that lets developers see through a robot\u2019s cameras and the engineering required before a robot can leave a demo floor to do something useful.<\/span><\/p>\n

As a robotics software engineer who leads the team behind <\/span>NVIDIA Isaac ROS<\/span><\/a> (Robot Operating System), Singh works on the connective tissue of the <\/span>physical AI<\/span><\/a> era. Built on the open source ROS 2 framework, Isaac ROS brings CUDA-accelerated libraries and AI models to developers building autonomous mobile robots, manipulation systems and humanoids.\u00a0<\/span><\/p>\n

\u201cMy goal is to make sure everyone feels like they are a part of the robotics future,\u201d Singh said.<\/span><\/p>\n

For Singh, that future began in middle school, building with LEGO Mindstorms, a popular line of programmable robotics kits. After excelling in robotics competitions throughout high school, he studied electrical engineering, computer science and business at the University of California, Berkeley, before joining NVIDIA full time after an internship with the robotics team.<\/span><\/p>\n

In a satisfying turn, the work he now leads began as his intern project.<\/span><\/p>\n

\u201cWe wanted to see what would happen if we just released some software as open source that uses the NVIDIA Jetson platform and NVIDIA CUDA libraries for robotics. Would there be any value there?\u201d Singh recalled. \u201cAnd the answer was, of course, yes, because developers always want to be able to unlock the full power of their GPUs.\u201d<\/span><\/p>\n

The result was Isaac ROS.<\/span><\/p>\n

The Building Blocks of a Robotics Revolution<\/span><\/h2>\n

Physical AI has long been a field of extraordinary imagination and stubborn, physics-bound realities. A clip of a robot dancing or executing complex balletics can travel the internet in hours. Building a system that works repeatedly, across sensors, platforms, factories and labs, is slower business.\u00a0<\/span><\/p>\n

For Singh and the Isaac ROS team, the next era of robotics relies on a full stack: simulation, training, accelerated computing, AI models, middleware and edge deployment.<\/span><\/p>\n

Isaac ROS supports manipulation, mobility and humanoids. It gives developers packages for perception, object detection, mapping, collision detection and motion planning, and it can run on workstations, <\/span>NVIDIA DGX Spark<\/span><\/a> personal AI supercomputers as well as <\/span>NVIDIA Jetson<\/span><\/a> edge systems.\u00a0<\/span><\/p>\n

\u201cCompared with the original Isaac SDK, Isaac ROS is completely modular,\u201d Singh said. \u201cWe ship the software like a bunch of LEGO bricks \u2014 you get to assemble them however you want, and you can easily combine our packages with existing ROS code written by you or others in the global robotics community.\u201d<\/span><\/p>\n

NVIDIA is making it easier for many robot builders to move faster, Singh said, and to do so on a foundation they can inspect, adapt and trust.<\/span><\/p>\n

\u201cThe main reason open source is valuable is because it gives people confidence that they can build upon this stack at this very initial stage,\u201d Singh said. \u201cBecause the entire landscape can shift so rapidly, developers need the confidence that this platform is still going to be there to modify and improve two or three years into the future.\u201d<\/span><\/p>\n

That confidence matters because robotics is changing quickly. Humanoid robots, in particular, have moved from science fiction to an active engineering frontier.<\/span><\/p>\n

\"\"<\/p>\n

Singh\u2019s team has been making Isaac ROS better suited to this moment, including for developers using AI agents and for humanoid systems that need an end-to-end software stack.<\/span><\/p>\n

NVIDIA\u2019s long history of work in robotics and farsighted vision for the field is what initially attracted Singh to the company \u2014 and made him all the more confident in his work upon joining.<\/span><\/p>\n

\u201cNVIDIA was here and working on this problem before anybody else thought it was important,\u201d he said. \u201cWe already had a stake in the ground.\u201d<\/span><\/p>\n

Open source, in Singh\u2019s view, is a way of sharing both confidence and responsibility. If a robotics startup builds on a closed system, it must trust that the system will still match its needs years later. With open software, developers can inspect the code, change it, contribute fixes and carry it forward. One company\u2019s bug fix becomes another company\u2019s acceleration.<\/span><\/p>\n

\u201cWhen more people can build robots,\u201d Singh said, \u201cthe future gets here faster.\u201d<\/span><\/p>\n

\"\"<\/p>\n

Follow <\/span><\/i>@nvidialife<\/span><\/i><\/a> on Instagram and learn more about <\/span><\/i>NVIDIA life, culture and careers<\/span><\/i><\/a>.\u00a0<\/span><\/i><\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

https:\/\/blogs.nvidia.com\/blog\/nvidia-life-jaiveer-singh\/<\/p>\n","protected":false},"author":0,"featured_media":4606,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4605"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=4605"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4605\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/4606"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=4605"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=4605"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=4605"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}