Core Viewpoint - Figure has made significant advancements in technology and financing after parting ways with OpenAI, achieving a post-financing valuation of $39 billion, the highest in the embodied intelligence sector to date [2][32]. Financing and Valuation - Figure has successfully raised over $1 billion in Series C financing, leading to a post-money valuation of $39 billion [2][32]. - The financing round was led by Parkway Venture Capital, with participation from notable investors including Nvidia, Brookfield Asset Management, and Qualcomm Ventures [4]. Strategic Focus Areas - The new funding will support Figure's development in three core areas [8]. - The first area is the large-scale penetration of humanoid robots into household and commercial scenarios, with plans to expand the production capacity of its BotQ manufacturing facility [9]. - The second area involves building next-generation GPU infrastructure to accelerate training and simulation for the Helix model [21]. - The third area focuses on launching advanced data collection projects to enhance the robot's understanding and operational capabilities in complex environments [21]. Technological Advancements - Figure has introduced the Helix architecture, a visual-language-action model that allows robots to perceive, understand, and act like humans [17]. - Helix consists of two systems that communicate and are trained end-to-end, enabling the robot to perform various tasks with a single unified model [18]. - The recent funding will further enhance the capabilities of Helix, which is designed to optimize the performance of embodied intelligent AI systems [20]. Company Background - Figure was founded in May 2022 by Brett Adcock, a serial entrepreneur [22]. - The company gained attention in the humanoid robotics sector after raising $675 million in Series B financing in February 2024, achieving a valuation of $2.6 billion at that time [22]. - Following a partnership with OpenAI, Figure decided to pursue vertical integration of its AI models, focusing on developing an end-to-end AI model tailored for specific robotic hardware [30][28].

Core Viewpoint - Figure robots are expanding their capabilities beyond folding clothes to include loading dishwashers, showcasing advancements in their Helix architecture and adaptability in handling various household tasks [1][7][11]. Group 1: Technological Advancements - Figure robots utilize the same Helix architecture for different tasks, such as package sorting and towel folding, without requiring new algorithms or special engineering, only additional data [4][20]. - The Helix architecture is a result of Figure's evolution after parting ways with OpenAI, designed as an end-to-end "vision-language-action" model that allows robots to perceive, understand, and act like humans [21][25]. - The system consists of two components that communicate through end-to-end training, enabling robust performance across various tasks using a single unified model [22][24]. Group 2: Task Complexity - Loading a dishwasher involves complex challenges, such as separating stacked dishes, adjusting angles, and coordinating dual-arm movements, which require precise operations due to the fragility and smoothness of items [16][17]. - Each loading scenario is unique, necessitating the system's ability to adapt and correct itself while maintaining stable performance [19]. - The tasks of loading dishes, sorting packages, and folding towels, while seemingly unrelated, can all be managed by the Helix architecture, demonstrating its versatility and potential for broader applications [25][26].

Core Viewpoint - Figure's humanoid robot has successfully learned to fold clothes using an end-to-end approach without any architectural changes, showcasing its adaptability and advanced capabilities in handling complex tasks [2][21][28]. Group 1: Robot Capabilities - The humanoid robot demonstrated its ability to fold towels smoothly, employing precise finger control and real-time adjustments during the process [7][18]. - This task is considered one of the most challenging dexterous operations for humanoid robots due to the variability and unpredictability of clothing shapes [15][16]. - The robot's performance in folding clothes was achieved using the same model and architecture as its previous task of package sorting, with the only change being the dataset used for training [14][28]. Group 2: Helix Architecture - The Helix architecture, developed after Figure's split from OpenAI, is a unified "visual-language-action" model that allows the robot to perceive, understand, and act like a human [21][22]. - Helix consists of two systems that communicate with each other, enabling the robot to perform various tasks with a single set of neural network weights [22]. - Key components of Helix include visual memory, state history, and force feedback, which enhance the robot's ability to adapt and respond to its environment [23][29]. Group 3: Future Plans - Figure plans to continue improving the robot's flexibility, speed, and generalization capabilities based on the expansion of real-world data [20]. - The company aims to develop the robot's ability to perform a complete set of household tasks, including washing, folding, and potentially hanging clothes [38].

Core Insights - The article discusses the debut of Figure's humanoid robot, which has learned to fold clothes using a neural network without any architectural changes, only by increasing the data input [1][21]. Group 1: Robot Capabilities - The humanoid robot demonstrated its ability to fold towels smoothly and efficiently, showcasing dexterous hand movements and real-time adjustments during the process [6][19]. - This task of folding clothes is considered one of the most challenging dexterous operations for humanoid robots due to the unpredictable nature of clothing [15][16]. - The robot operates in an end-to-end manner, processing visual and language inputs to execute precise motor controls [8][19]. Group 2: Helix Architecture - The Helix architecture is pivotal for the robot's performance, allowing it to autonomously fold clothes without modifying the model or training hyperparameters, relying solely on a new dataset [21][22]. - Helix consists of two systems that communicate with each other, enabling the robot to perform various tasks using a unified model and a single set of neural network weights [23]. - Key components of Helix include visual memory, state history, and force feedback, which enhance the robot's ability to perceive and interact with its environment effectively [24][28][29]. Group 3: Future Developments - Figure plans to enhance the robot's flexibility, speed, and generalization capabilities based on the expansion of real-world data [20]. - The company aims to continue improving the robot's performance in various tasks, building on the success of its current capabilities [20][23].