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    Home»Robotics»Reworking robotics with modular expertise
    Robotics

    Reworking robotics with modular expertise

    Arjun PatelBy Arjun PatelMarch 7, 2026No Comments3 Mins Read
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    Episode 234 of The Robotic Report Podcast options  Dinesh Narayanan, Head of Commercialization, Common Robotics.

    Dinesh Narayanan, Head of Commercialization, Common Robotics.

    Dinesh shared insights into the founding of Common Robotics, highlighting the transition from Microsoft and the event of their proprietary platform, GRID.

    This platform goals to streamline the deployment and scaling of robotics options by integrating simulation, AI fashions, and deployment pipelines.

    Dinesh emphasised the significance of adaptability in AI strategies and the corporate’s give attention to offering fast prototyping and deployment capabilities.

    The dialog additionally touched on the potential of humanoid robots and the function of GRID in facilitating their growth.

    Dinesh concluded by discussing the corporate’s enterprise mannequin and future aspirations, drawing parallels to the influence of platforms like AWS and Azure within the tech trade.


    Present timeline

    • 6:10 – Information of the week
    • 25:10 – Dinesh Narayanan, Head of Commercialization, Common Robotics

    Information of the week

    This overhead illustration of the incident shows the location of the Waymo vehice (ADS-V) and the stopped school bus.

    This overhead illustration of the incident exhibits the situation of the Waymo car (ADS-V) and the stopped college bus. | Credit score: NTSB

    • Federal investigators have launched a probe into Waymo after a distant operator’s error precipitated a self-driving taxi to illegally move a stopped college bus in Austin, marking the newest in a collection of security violations involving the corporate’s autonomous fleet.
    • Whereas the autonomous driving expertise is marketed as a approach to eradicate human error, this particular incident reveals a “human-in-the-loop” failure. The car did precisely what it was purported to do: it stopped and requested for assist. However the security web failed when the distant human operator gave it the inexperienced gentle to proceed illegally.
    • The incident in query befell on January 12, 2026, about 7:55 a.m. Central Normal Time. In line with the NTSB, a 2024 Jaguar I-Tempo sport utility car, outfitted with an ADS and operated by Waymo LLC, handed a 2025 Thomas Constructed college bus, operated by Austin Impartial College District (Austin ISD), loading pupil passengers in Austin, Travis County, Texas.
    • What makes this case chilling is that, in accordance with the report, the autonomous car stopped and requested its human security operator: “Is that this a college bus with lively alerts?” The human distant agent responded “No”.
    • Attention-grabbing put up by Fortune that includes analysis on a purported relationship between minimal wage hikes and elevated automation curiosity in producers.
    • Quote from the article: “The central discovering is exact and constant: A ten% improve within the minimal wage is related to an roughly 8% improve within the chance a producing agency will undertake industrial robots, relative to the typical adoption fee within the pattern.​”
    • “Corporations topic to increased minimal wages usually tend to undertake robots,” the authors wrote, “even after controlling for observable agency and native financial traits.”
    • The analysis doesn’t make any conclusions about whether or not employees lose their jobs on account of the wage hike or robotic set up. ​
    • The examine focuses on the connection between minimal wage will increase and the chance of robotic adoption by manufacturing companies. ​
    • Whereas it acknowledges that robots could alter the construction of employment, particularly in low-wage sectors, it explicitly states that the examine doesn’t contemplate labor outcomes, corresponding to employment results. ​
    • The authors recommend that future analysis may study how robotic adoption interacts with labor market establishments, employee outcomes, and agency efficiency over longer time horizons.


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