Figure人形机器人首秀灵巧手叠衣服！神经网络架构不变，只增加数据集就搞定

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].