在「想象」中练就真机能力：RISE，让VLA强化学习告别真机试错

Core Insights - The article discusses the development of the Visual-Language-Action (VLA) model as a core framework for general operational tasks in embodied intelligence, highlighting its challenges in complex scenarios such as long-term planning and dynamic interactions [2][8] - The RISE (χ0-RL) framework proposed by the OpenDriveLab team aims to address these challenges by enabling robots to perform reinforcement learning in an imagined virtual space, significantly improving long-term task performance [2][18] Challenges in VLA Implementation - The reliance on imitation learning leads to cumulative errors in reasoning, as current VLA models primarily learn from successful paths demonstrated by experts, making it difficult to self-correct when deviating from these paths [9][10] - Real-world reinforcement learning faces three major constraints: high costs of physical interactions, safety risks associated with exploratory operations, and the lack of automatic reset mechanisms in real environments [11][13] - Existing world models struggle to balance high-fidelity simulation and long-term consistency, limiting the effectiveness of virtual-physical integration attempts [8][11] RISE Framework Overview - RISE utilizes a combination of world models to facilitate online learning without the need for extensive physical interactions, leading to significant improvements in real-world task performance [15][18] - The framework's core innovation lies in transferring physical interactions to a combination world model, creating a self-evolving cycle in imagined spaces [16][17] Components of RISE - The combination world model consists of two independent modules: a controllable dynamics model for high-fidelity physical simulation and a progress value model for precise trajectory evaluation [18] - The controllable dynamics model employs a task-centric batching strategy to focus on relevant actions, while the progress value model integrates progress estimation and temporal difference learning to enhance sensitivity to minor failures [18] Self-Evolution in Imagined Spaces - RISE implements a three-step online reinforcement learning loop entirely within the imagined space, allowing for efficient strategy iteration without real-world interactions [19][20] - The process includes generating future video predictions, evaluating imagined trajectories, and updating the VLA strategy based on high and low-value actions [20] Performance Evaluation - RISE has been tested on three challenging real-world long-term tasks: dynamic brick sorting, backpack packing, and box closing, demonstrating significant performance improvements across all metrics [24][25] - The success rates for these tasks increased dramatically, with dynamic brick sorting rising from 50% to 85%, backpack packing from 30% to 85%, and box closing achieving a success rate of 95% [29] Generalization and Robustness - The strategies developed through RISE exhibit the ability to recover from failures and adapt to unexpected disturbances, showcasing a level of intelligence beyond mere imitation [28][29] - The model's capacity for position generalization allows it to perform tasks accurately even when the placement of objects changes, without requiring retraining [31] Quality of Generation - RISE's dynamics model outperforms baseline models in generating high-fidelity future frames, maintaining physical consistency and avoiding common issues such as blurriness or object teleportation [32][34] Future Implications - RISE represents a paradigm shift in how intelligent agents understand and interact with the world, moving from passive adaptation in the physical realm to active evolution in imagined spaces [35][36] - This framework significantly reduces the costs associated with physical interactions, paving the way for more efficient training and deployment of robotic systems [36][37]