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Clone’s Protoclone is a musculoskeletal android engineered around an anatomically based human skeletal structure. It incorporates more than 1,000 artificial muscle actuators (Myofibers), polymer-based bones with articulated joints and ligaments, and a hydraulic vascular system for force transmission. The platform integrates depth cameras, inertial and pressure sensors, and onboard...

10,792 görüntüleme • 7 ay önce •via X (Twitter)

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NEWS: Humanoid robotics company Figure has released Helix 02, what they claim in their most capable humanoid model yet. "A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room: • Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date. • All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network. • Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion. • New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion. Helix 02 is trained on over 1,000 hours of human motion data and integrates vision, touch, and proprioception."

Sawyer Merritt

624,910 görüntüleme • 8 ay önce

Prime Minister Shehbaz Sharif, today, launched the Artificial Intelligence (AI)-based Prime Minister Office System (PMOS), describing it as a transformative digital platform that will modernise governance by recording, communicating, monitoring and tracking every directive issued by the Prime Minister from issuance through implementation, verified completion and post-completion monitoring. Addressing the launch ceremony at the Prime Minister's Office, the prime minister directed all relevant authorities to ensure the immediate and full implementation of the new AI-based system, making it clear that traditional paper-based working would be replaced by digital processes. "From today onwards, I will not expect any of you with a file in your hands. Everything will be discussed through this mechanism," the prime minister said. He praised Minister for Information Technology and Telecommunication Shaza Fatima Khawaja and Secretary IT for their efforts in developing and operationalising the system. He urged all ministries and government departments to adopt the platform, saying it would significantly improve the government's ability to deliver results. "This would make service delivery easier," he remarked. The ceremony was attended by Minister for Economic Affairs Ahad Khan Cheema, Minister for Information Technology Shaza Fatima Khawaja, and other senior officials. During the ceremony, the prime minister was given a comprehensive briefing on the AI-powered platform and its objectives. The prime minister was informed that PMOS was the first government platform in Pakistan to integrate artificial intelligence as a central component of decision-making and implementation monitoring, calling it a trailblazer for the digital transformation of the federal government. According to the briefing, PMOS had been developed at the centre of government to ensure efficient communication of the prime minister's directives throughout the government machinery while enabling timely implementation in accordance with defined quality standards. The system also serves as a secure technology-driven decision-support platform by integrating analytics and structured information within a protected IT environment. During the briefing, it was informed that PMOS would facilitate continuous policy calibration by enabling the government to utilise technological, human and financial resources more effectively to generate better economic outcomes. By introducing the platform at the Prime Minister's Office, the government intends to embed governance reforms at the highest level before extending them across the wider administrative structure. At the heart of the platform is a comprehensive digital repository that records every directive issued by the prime minister together with the complete chain of implementation, making PMOS both an institutional knowledge base and a permanent record of government decision-making. Unlike conventional government tracking systems, the briefing explained, PMOS incorporates an artificial intelligence layer capable of reading, classifying and analysing directives rather than merely storing them.

Prime Minister's Office

151,225 görüntüleme • 2 ay önce

Can an inexpensive, off-the-shelf IMU be the only sensor to estimate the full state (position, velocity, orientation) of a quadrotor flying through a track at high speed and even be on-pair with vision-based localization? The answer is yes, within certain limitations! In this #RAL2023 paper, we propose a learning-based odometry algorithm that couples a model-based filter driven by the inertial measurements with a learning-based module with access to the control commands. Our system outperforms by a large margin the state-of-the-art visual-inertial odometry (#VIO) algorithms and the state-of-the-art learned-inertial odometry algorithm, #TLIO, for the task of drone racing. Additionally, we show that our system is as accurate as a VIO algorithm that uses a camera to localize to a known map of the racing track. The main limitation of our approach is that it cannot generalize to trajectories that have not been seen at training time. However, in drone racing competitions, the track is known beforehand. Human pilots spend hours or even days of practice on the race track before the competition. Similarly, our system can be trained with the data collected during practice time and deployed during the competition. Future work will investigate how to generalize to trajectories not seen at training time. The code is released! Paper: Video: Code: Kudos to Giovanni Cioffi Leonard Bauersfeld Elia Kaufmann European Research Council (ERC) University of Zurich UZH Science UZH Space Hub NCCR Robotics Aerial Core #RAL2023 #IROS2023 #SLAM

Davide Scaramuzza

37,061 görüntüleme • 3 yıl önce