Research Background and Core Issues - The mainstream paradigm in embedded intelligence is learning robot operations from human demonstrations, but the morphological differences between human hands and various robotic hands (e.g., 2-finger, 3-finger, 5-finger) pose a significant barrier to technology implementation [3] - The core goal of UniBYD is to establish a learning paradigm that transcends mere imitation of human actions, enabling robots to autonomously discover operational strategies that match their physical characteristics, thus achieving efficient generalization across different robotic hand forms [3] Core Innovation: UniBYD Framework Design - UniBYD is a unified reinforcement learning framework that facilitates a smooth transition from imitation to exploration through three core components: unified morphological representation, dynamic reinforcement learning mechanism, and fine imitation guidance [5] Unified Morphological Representation (UMR) - UMR addresses modeling differences among various robotic hand morphologies by unifying dynamic states and static attributes into a fixed-dimensional representation [7] - Dynamic state processing involves fixing the wrist state to 13 dimensions (position, posture, speed) and padding joint states to the maximum degrees of freedom, using trigonometric encoding to avoid wrapping issues [8] Dynamic PPO: Gradual Learning from Imitation to Exploration - Traditional imitation learning is limited to replicating human actions, resulting in performance far below human levels due to physical differences [10] - Dynamic PPO utilizes a reward annealing mechanism and loss collaborative balance to achieve a smooth transition from imitating humans to autonomous exploration [12] Reward Mechanisms - The reward structure includes imitation rewards, which quantify the similarity between the current state and human demonstration across multiple dimensions, and goal rewards, which are given only upon successful task completion [13][14] - The total reward is a weighted sum of these two types, with weights dynamically adjusted based on training progress and success rates [15] Loss Collaborative Balance - To ensure effective exploration and physical feasibility, two types of losses are incorporated into the PPO objective: entropy regularization to encourage exploration and boundary loss to prevent actions from exceeding physical limits [16][17] Mixed Markov Shadow Engine: Fine Guidance in Early Imitation - The shadow engine addresses early training challenges by combining action mixing and object-assisted control, ensuring stability during the initial phases of training [20] Performance Validation - The UniManip benchmark is designed as the first cross-morphological robotic operation benchmark, covering 29 types of single/double-hand tasks adaptable to 2-finger, 3-finger, and 5-finger robotic hands [25] - The framework demonstrates high success rates across all hand morphologies, with a 67.9% improvement over existing methods, and significant reductions in position and orientation errors [28] Real-World Transfer: Effectiveness from Simulation to Physical Robots - The framework was validated on various robotic hands, achieving success rates of 52% for 2-finger, 64% for 3-finger, and 70% for 5-finger robots, showcasing adaptability to different hardware characteristics [34] Core Conclusions and Significance - UniBYD represents a paradigm shift by moving beyond the limitations of "copying human actions" to propose a "morphological adaptation strategy" learning paradigm, facilitating a smooth transition from imitation to exploration through dynamic reinforcement learning [39] - The unified morphological representation enables the framework to directly adapt to various robotic hand forms, addressing the core challenges of cross-morphological generalization [39] - The performance significantly surpasses state-of-the-art methods and successfully transfers to real-world robots, providing a universal solution for diverse robotic operation tasks [39]