Imitation based robot learning has recently gained significant attention in the robotics field due to its theoretical potential for transferability and generalizability. However, it remains notoriously costly, both in terms of hardware and data collection, and deploying it in real-world environments demands meticulous setup of robots and precise experimental conditions. In this paper, we present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots, not just expensive collaborative robotic arms. Furthermore, our results show that multi-task robot learning is achievable with simple network architectures and fewer demonstrations than previously thought necessary. As the current evaluating method is almost subjective when it comes to real-world manipulation tasks, we propose Voting Positive Rate (VPR) - a novel evaluation strategy that provides a more objective assessment of performance. We conduct an extensive comparison of success rates across various self-designed tasks to validate our approach. To foster collaboration and support the robot learning community, we have open-sourced all relevant datasets and model checkpoints, available at huggingface.co/ZhiChengAI.

本研究解决了模仿学习在机器人领域中应用成本高昂的问题，提出了一种低成本、易于复制和适应不同机器人及环境的学习框架。通过展示在工业机器人上成功应用该框架，研究表明即使使用简单的网络结构和较少的演示，亦可实现多任务机器人学习。此外，本研究还提出了投票正率（VPR）这一新评价策略，以提供对真实操作任务更客观的性能评估。

通用机器人学习框架