Dyna-2：1時間の自律性を備えた汎用物理エージェント — ジェイソン・マー、Dyna Robotics
Dynaのチーフサイエンティストとともに、ワールドアクションモデルと物理AIの次なるフロンティアへの大きな賭けに挑む当社初のロボティクス・ポッド
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Dyna-2：1時間の自律性を備えた汎用物理エージェント — ジェイソン・マー、Dyna Robotics
Dynaのチーフサイエンティストとともに、ワールドアクションモデルと物理AIの次なるフロンティアへの大きな賭けに挑む当社初のロボティクス・ポッド
Gemini 4 Argon for Robotics
Gemini 4 Argon for Robotics
翻訳待ち・原文を表示中 · enBuilding AI for Reliable Execution: Lessons From Industrial Robotics
Inside Standard Bots’ AI stack, pretrained models learn factory tasks from demonstrations and improve through corrections from real deployments.
翻訳待ち・原文を表示中 · enGemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.
翻訳待ち・原文を表示中 · enGemini Robotics 2 brings whole body intelligence to robots
Gemini Robotics 2 brings whole body intelligence to robots
翻訳待ち・原文を表示中 · enImport AI 466: The bitter lesson for robotics, AIs complete week-long programming tasks; and OpenAI's accidental AI hacker
The warning shots will continue until civilization wakes up
翻訳待ち・原文を表示中 · enNVIDIA Cosmos-H-Dreams: 外科ロボティクスにリアルタイムの生成シミュレーションをもたらす
NVIDIA Cosmos-H-Dreams: 外科ロボティクスにリアルタイムの生成シミュレーションをもたらす
Empowering India’s next generation of innovators with ATL Saathi
Google and AIM launched ATL Saathi, a Gemini-powered AI tool empowering Indian educators in robotics labs.
翻訳待ち・原文を表示中 · enPowering the future of robotics in Europe
Powering the future of robotics in Europe
翻訳待ち・原文を表示中 · enGemini Robotics-ER 1.6: Powering real-world robotics tasks through enhanced embodied reasoning
Gemini Robotics ER 1.6: Enhancing spatial reasoning and multi-view understanding for autonomous robotics.
翻訳待ち・原文を表示中 · enInformation-Driven Design of Imaging Systems
An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks. What matters in these systems is not how measurements look, but how much useful information they contain. AI can extract this information even when it is encoded in ways that humans cannot
翻訳待ち・原文を表示中 · en