Figure人形机器人工作场景。图片来源：JOHN KOETSIER 当地时间9月16日，人形机器人公司Figure AI宣布，已在C轮融资中筹集"超10亿美元"。资金将用于扩大机器人产能、建设英 伟达（NVIDIA）GPU算力基础设施，进而加速训练与模拟进程、拓展人类工作生活场景的数据采集。 最终目标是量产可交付的硬件，也就是机器人；打造让机器人变得智能的AI引擎，以及收集该引擎运行所需的训练数据。 "Figure的目标是攻克通用机器人的难题，"CEO布雷特·阿德考克（Brett Adcock）当天在一段YouTube视频中表示，"历史发展到今天，我 们头一回具备了实现这一目标的技术条件。" Figure的志向极为远大。其Figure 02机器人去年年底成为全球第二个获得有偿工作的人形机器人。今年早些时候，阿德考克宣布，公司 计划在未来四年交付10万台人形机器人，并补充说Figure的客户名单中已经包含了"美国最大企业之一"。 Figure的野心远不止于仓库与工厂。 Figure定期发布机器人在厨房工作、端送饮品、将碗碟放进洗碗机、折叠衣物及执行其他家务的视频。而支撑这一切的核心是Helix AI ——Fi ...

Core Viewpoint - The article highlights the advancements in robotics, particularly focusing on the capabilities of the Helix system developed by Figure, showcasing its ability to handle a wider variety of packages with improved efficiency and accuracy [1][7][19]. Technical Improvements - The Helix system has undergone significant enhancements due to the expansion of high-quality demonstration datasets and architectural improvements in its visuo-motor policy, leading to increased stability under high-speed workloads [7][20]. - The introduction of state awareness and force sensing has enhanced the robustness and adaptability of the robots without sacrificing efficiency [8]. Data Expansion - The range of packages that the Helix system can handle has expanded to include not only standard cardboard boxes but also polyethylene bags, envelopes, and other flexible or crumpled items [10]. - The system has developed adaptive strategies for different package shapes, such as flipping cardboard boxes with both hands or gently pinching the edges of envelopes [13][15]. Performance Metrics - The average processing speed for packages is approximately 4.05 seconds, with throughput increasing by 58% and barcode success rates rising from 88.2% to 94.4% [17][30]. - The improvements indicate a more agile and reliable system capable of operating at speeds and accuracy levels closer to human performance [19]. Architectural Enhancements - The Helix system's architecture has been improved with new memory and sensing modules, enhancing its ability to perceive environmental changes [20]. - Key components include: - **Visual Memory**: Allows the robot to recall previous frames to locate barcodes effectively [22][25]. - **State History**: Enables the robot to maintain context during actions, improving its ability to correct movements quickly [26][27]. - **Force Feedback**: Provides tactile feedback to adjust movements dynamically, enhancing control and adaptability [28]. Human Interaction - The Helix system can autonomously sort packages and establish human-robot interaction without separate programming, recognizing cues from humans to hand over items [31][33]. Community Response - The release of the unedited 60-minute video has generated significant interest and discussion among viewers, with varied opinions on the implications of robotics in logistics and the future of human jobs [34][37][38].

Core Insights - The article highlights the advancements in robotics, particularly focusing on the capabilities of the Helix system developed by Figure, which showcases improved performance in handling various types of packages in logistics [1][7][19]. Technical Improvements - The Helix system has undergone significant enhancements due to the expansion of high-quality demonstration datasets and architectural improvements in its visuo-motor policy, leading to increased stability under high-speed workloads [7][19]. - The system can now handle a wider variety of package shapes and materials, including polyethylene bags and envelopes, demonstrating its adaptability [10][17]. - The introduction of real-time data observation allows the robot to learn and adjust its actions dynamically, improving its efficiency and accuracy [2][8]. Performance Metrics - The average processing speed for packages is approximately 4.05 seconds, with throughput increasing by 58% and barcode scanning success rates rising from 88.2% to 94.4% [17][30]. - The Helix system's new strategies have led to a success rate of 94% for barcode orientation and maintained an accuracy of over 92% [30]. System Architecture - The Helix system incorporates three main components: visual memory, state history, and force feedback, enhancing its ability to perceive and interact with its environment [20][22]. - Visual memory allows the robot to recall previous frames to locate barcodes effectively, while state history helps maintain context during operations [23][27]. - Force feedback enables the robot to adjust its movements based on tactile information, improving control and adaptability to different package weights and shapes [28]. Human Interaction - The Helix system can seamlessly engage in human-robot interaction without the need for separate programming, recognizing cues from humans to hand over packages [31][33]. Community Reactions - The release of the unedited 60-minute video showcasing the robot's capabilities has sparked discussions among viewers, with some praising the transparency and others questioning the implications for human labor in logistics [34][37][38].