CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence
This work addresses the problem of scalability and adaptability in robotic learning for researchers and practitioners, though it appears incremental as it builds upon existing frameworks like LeRobot.
The paper tackles the challenge of achieving generalization and transfer in robotic learning across diverse platforms and environments by introducing a generalized end-to-end framework, which demonstrated superior performance and generalizability compared to the LeRobot framework in experiments on seven manipulation tasks.
Physical intelligence holds immense promise for advancing embodied intelligence, enabling robots to acquire complex behaviors from demonstrations. However, achieving generalization and transfer across diverse robotic platforms and environments requires careful design of model architectures, training strategies, and data diversity. Meanwhile existing systems often struggle with scalability, adaptability to heterogeneous hardware, and objective evaluation in real-world settings. We present a generalized end-to-end robotic learning framework designed to bridge this gap. Our framework introduces a unified architecture that supports cross-platform adaptability, enabling seamless deployment across industrial-grade robots, collaborative arms, and novel embodiments without task-specific modifications. By integrating multi-task learning with streamlined network designs, it achieves more robust performance than conventional approaches, while maintaining compatibility with varying sensor configurations and action spaces. We validate our framework through extensive experiments on seven manipulation tasks. Notably, Diffusion-based models trained in our framework demonstrated superior performance and generalizability compared to the LeRobot framework, achieving performance improvements across diverse robotic platforms and environmental conditions.