Robotics

What changed

Recent signals

• DeepMind demonstrates whole-body control; ER 2 has developer access while VLA and on-device models remain early-access. Demonstrated embodiments and bounded access do not establish general commercial humanoid autonomy.retrospective mapping
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• The reasoning model is offered through the Gemini API while the action model remains available to selected partners. API access to reasoning is distinct from unrestricted access to robot actuation models.retrospective mapping
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• DeepMind introduces local robot inference and an adaptation SDK for selected testers. Selected-access demonstrations do not prove general commercial deployment or cross-embodiment reliability.retrospective mapping
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• Multimodal robot models and customizable humanoid resources broadened research access across embodiments without establishing universal commercial autonomy.retrospective mapping
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• Vision-language-action flow-model research expanded manipulation across embodiments, without proving general commercial robot autonomy.retrospective mapping
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• Simulation, orchestration and teleoperation tooling supported humanoid development; announced training systems remained distinct from autonomous deployment.retrospective mapping
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• Open robot-policy adaptation research progressed alongside a multi-year commercial humanoid logistics agreement, with bounded tasks distinct from general autonomy.retrospective mapping
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• Robot-learning data and policies began scaling across many physical embodiments through shared datasets and cross-robot policy training.retrospective mapping
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• Web-scale vision-language representations were adapted directly into robot-action models, strengthening semantic transfer from general models into physical control.retrospective mapping
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• Robotics research began using newly generated experience as part of iterative multi-task, multi-embodiment training loops, while remaining bounded research rather than autonomous self-improvement.retrospective mapping
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• Real-world robot learning showed a clearer scaling path through large, diverse demonstration datasets and transformer policies covering hundreds of language-conditioned tasks, without establishing general physical autonomy.retrospective mapping
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• LLM-mediated robotics progressed toward closed-loop replanning and executable program synthesis over robot APIs, strengthening embodied agent-like capability without establishing general persistent autonomy.retrospective mapping
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• Language/generalist transformer models began to select and execute actions in embodied or interactive environments, materially strengthening an agent-like precursor without yet establishing persistent autonomous agents.retrospective mapping
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• Physical and spatial AI are moving into major chip-vendor strategy, tightening the link between AI models, robotics, simulation and hardware roadmaps.
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• Agent safety is expanding from model behavior into runtime, hardware, identity and tool-access controls.
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