CopterTime / КоптерТайм

We are proud to announce the launch of V-SCAN, an innovative software designed to enhance drone camera capabilities by enabling precise scanning and tracking of multiple moving objects simultaneously. V-Scan is engineered to seamlessly integrate with drone cameras, offering superior tracking accuracy and real-time analysis. Whether monitoring crowds, managing traffic, security at large-scale events, or even ensuring your perimeter defence, V-Scan delivers reliable and comprehensive coverage. Diverse Applications of V-Scan 1. Public Safety and Security: V-Scan is an invaluable tool for law enforcement and security agencies. During public events such as concerts, sports games, and festivals, the software can monitor large crowds, identify potential threats, and ensure swift response to emergencies. 2. Traffic Management: Urban planners and transportation authorities can utilize V-Scan to oversee traffic flow, detect congestion, and manage incidents in real-time. This capability helps reduce traffic jams and enhances road safety. 3. Disaster Response: In natural disasters such as floods, wildfires, and hurricanes, V-Scan-equipped drones can quickly scan affected areas, track the movement of people and vehicles, and assist in coordinating rescue operations. 4. Wildlife Conservation: Conservationists can employ V-Scan to monitor wildlife in their natural habitats. The software can track the movement of animals, study their behavior, and help in the protection of endangered species. 5. Event Management: Organizers of large-scale events can leverage V-Scan to oversee crowd dynamics, manage entry and exit points, and ensure the safety of attendees. Contact your local dealers or visit Volarious' website now! For more information about Volarious, please visit www.volarious.com #drones #drone #publicsafety #lawenforcement #tethereddrone

Stanford HumanPlus Robot: Humanoid Shadowing and Imitation from Humans Project website:

Team at Stanford University: Zipeng Fu*, Qingqing Zhao*, Qi Wu*, Gordon Wetzstein, Chelsea Finn Abstract: One of the key arguments for building robots that have similar form factors to human beings is that we can leverage the massive human data for training. Yet, doing so has remained challenging in practice due to the complexities in humanoid perception and control, lingering physical gaps between humanoids and humans in morphologies and actuation, and lack of a data pipeline for humanoids to learn autonomous skills from egocentric vision. In this paper, we introduce a full-stack system for humanoids to learn motion and autonomous skills from human data. We first train a low-level policy in simulation via reinforcement learning using existing 40-hour human motion datasets. This policy transfers to the real world and allows humanoid robots to follow human body and hand motion in real time using only a RGB camera, i.e. shadowing. Through shadowing, human operators can teleoperate humanoids to collect whole-body data for learning different tasks in the real world. Using the data collected, we then perform supervised behavior cloning to train skill policies using egocentric vision, allowing humanoids to complete different tasks autonomously by imitating human skills. We demonstrate the system on our customized 33-DoF 180cm humanoid, autonomously completing tasks such as wearing a shoe to stand up and walk, unloading objects from warehouse racks, folding a sweatshirt, rearranging objects, typing, and greeting another robot with 60-100% success rates using up to 40 demonstrations.

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