{"id":23578,"date":"2026-08-27T22:15:43","date_gmt":"2026-08-27T22:15:43","guid":{"rendered":"https:\/\/scannn.com\/anthropics-new-hardware-standard-lets-ai-agents-control-the-physical-world\/"},"modified":"2026-08-27T22:15:43","modified_gmt":"2026-08-27T22:15:43","slug":"anthropics-new-hardware-standard-lets-ai-agents-control-the-physical-world","status":"publish","type":"post","link":"https:\/\/scannn.com\/lv\/anthropics-new-hardware-standard-lets-ai-agents-control-the-physical-world\/","title":{"rendered":"Anthropic's new hardware standard lets AI agents control the physical world"},"content":{"rendered":"\n<div>\n<p>Anthropic gave the example of a model like Claude adjusting a laser, checking the results via a separate camera, then repeating the process to automatically calibrate the whole system. MHS could also allow an AI model to focus a microscope, analyze the results, decide what part needs more observation, then automatically move the microscope to the relevant section to continue the experiment.<\/p>\n<p>In a video, Anthropic also showed Claude reasoning how to get a robotic arm to pick up an aluminum can even though it had not been specifically trained on the required steps. And rather than reasoning through each step each time, Anthropic says MHS-enabled models can sequence steps across instruments by writing API scripts and adjusting them as conditions require.<\/p>\n<figure class=\"ars-video ars-video--horizontal\">\n<div>\n<p><iframe title=\"AI models can now help run physical science experiments\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube.com\/embed\/P1zBiAQU1IA?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<div class=\"caption font-impact dusk:text-gray-300 mb-4 mt-2 inline-flex flex-row items-stretch gap-1 text-base leading-tight text-gray-400 dark:text-gray-300\">\n<p>\n      Anthropic introduces MHS in a promo video.<\/p>\n<\/p><\/div>\n<\/div>\n<\/figure>\n<p>Anthropic says MHS also includes a standardized tagging system to describe hardware\u2019s real-world constraints for models that may have been trained more in the virtual world. That includes encoded information about the hardware\u2019s physical characteristics (e.g., the weight and range of a robot arm) as well as its adjustable parameters, measurement options, and enforced safety limits. These tags can then be integrated into a reference file that can quickly provide an AI model with crucial information about a device it has no previous training experience with.<\/p>\n<p>For now, Anthropic says it is working with \u201ca first group of scientific research labs and advanced manufacturers\u201d during an MHS preview period, including Amazon Web Services (<a href=\"https:\/\/aws.amazon.com\/blogs\/opensource\/building-intelligent-physical-ai-from-edge-to-cloud-with-strands-agents-bedrock-agentcore-claude-4-5-nvidia-gr00t-and-hugging-face-lerobot\/\">Strands Robots<\/a>), Hugging Face (<a href=\"https:\/\/huggingface.co\/lerobot\">LeRobot<\/a>), Raspberry Pi, Automata, and Universal Robots. These partners will help Anthropic \u201cbuild safety evaluations and develop best practices for AI systems operating physical equipment,\u201d the company writes. After that, the plan is for MHS to eventually become an open source and \u201cagent agnostic\u201d standard for integrating AI and physical systems.<\/p>\n<p>In early testing with scientific partners over the past year, Anthropic says it \u201csaw MHS reduce the time it took to integrate devices, mak[ing] it possible to iterate faster in a variety of experimental settings.\u201d<\/p>\n<p>\u201cIf you can test hypotheses faster, you could create general technologies faster,\u201d Kemeny said in <a href=\"https:\/\/www.youtube.com\/watch?v=UxJZrCFzTHY\">a promo video<\/a> alongside the announcement. \u201cThis is how a century of progress can condense into a decade.\u201d<\/p>\n<\/p><\/div>\n<p><a href=\"https:\/\/arstechnica.com\/ai\/2026\/08\/anthropics-new-hardware-standard-lets-ai-agents-control-the-physical-world\/?utm_source=tldrnewsletter\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Anthropic gave the example of a model like Claude adjusting a laser, checking the results via a separate camera, then repeating the process to automatically calibrate the whole system. MHS could also allow an AI model to focus a microscope, analyze the results, decide what part needs more observation, then automatically move the microscope to [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":23579,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[143],"tags":[],"class_list":["post-23578","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"_links":{"self":[{"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/posts\/23578","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/comments?post=23578"}],"version-history":[{"count":0,"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/posts\/23578\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/media\/23579"}],"wp:attachment":[{"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/media?parent=23578"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/categories?post=23578"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/scannn.com\/lv\/wp-json\/wp\/v2\/tags?post=23578"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}