- Robotics researchers move under Chief Research Officer Mark Chen; applied robotics and hardware join Greg Brockman's Applied org
- The decision was so abrupt that OpenAI's booked CoRL booth sat empty
- The rationale is that general-purpose models may replace specialized robotics AI, not an exit from robotics
AI CURATED HOT STORY · 11/14/2026
OpenAI Reorganizes Robotics Team to Bet on General Models
OpenAI has confirmed the disbandment of its standalone robotics team, merging researchers into the core research group led by Chief Research Officer Mark Chen and moving the hardware team to Greg Brockman's applications division. The company stated the reorganization aims to accelerate R&D rather than exit robotics, with future plans to use general-purpose multimodal models as the robot's "brain," reducing reliance on specialized robot models.
A study by NVIDIA shows that an agent powered by GPT-6 Astra achieved a 74.8% success rate in simulated manipulation tests, significantly higher than the 51.0% of specialized models. This data supports OpenAI's bet. However, inference speed, cloud connectivity dependence, and on-site data accumulation remain bottlenecks. The abruptness of the decision is evident from the vacant pre-booked CoRL booth.
OpenAI's move reflects its core judgment that advances in general large models may render specialized robot AI unnecessary. If successful, robot capabilities would be driven directly by general models, lowering development barriers and accelerating iteration. For the robotics industry, this signals a potential paradigm shift and a reshuffling of the competitive landscape around general model capabilities.
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Total 2 reports · Latest first- The DecoderT2·72 pts
- IT之家 · 智能时代T2·78 pts
- OpenAI dissolved its standalone robotics unit, moving researchers under CRO Mark Chen and hardware into Greg Brockman's applications division
- OpenAI says general models like GPT-6 Astra already handle some robotic tasks, aiming to replace robotics-specific models
- Nvidia research shows Astra agents at 74.8% success on simulated manipulation versus 51.0% for specialized models, with speed and cloud dependency still limiting