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].