Core Insights - The article discusses the significance of the Conference on Robot Learning (CoRL) and highlights the achievement of a Chinese research team winning the Outstanding Paper Award for their work on UniFP, a unified control algorithm for legged robots [1][3]. Group 1: Conference Overview - CoRL is a leading academic conference in AI and robotics, showcasing cutting-edge research in robot learning [1]. - In 2025, CoRL received nearly 1,000 submissions, with 264 papers accepted after rigorous review [1]. Group 2: UniFP Algorithm - UniFP (Unified Force and Position Control Policy) is the first algorithm in legged robotics to unify force and position control within a single framework, overcoming traditional limitations [3][4]. - The algorithm is based on biomechanical impedance control principles, allowing robots to respond to environmental forces similarly to human muscle perception [3][4]. Group 3: Control Framework - The UniFP framework consists of three core modules: observation encoder, state estimator, and actuator, forming a complete control loop of perception, decision-making, and execution [7]. - The observation encoder processes historical state information, while the state estimator infers unmeasurable key states, enabling "sensorless force perception" [7][8]. Group 4: Performance Validation - The research team validated UniFP in various simulated contact scenarios and later on the Unitree B2-Z1 quadruped robot, demonstrating impressive multi-functional capabilities [8][10]. - In experiments, UniFP showed precise force control, adaptive force tracking, and compliant impedance control, outperforming traditional methods [10][11]. Group 5: Imitation Learning - UniFP's integration with imitation learning significantly enhances the robot's learning efficiency in contact-intensive tasks, achieving a success rate improvement of approximately 39.5% over traditional methods [11][13]. - The research demonstrated UniFP's versatility across different robot forms and tasks, confirming its generalizability [13][14].