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Complex and contact-rich robotic manipulation tasks, particularly those thatinvolve multi-fingered hands and underactuated object manipulation, present asignificant challenge to any control method. Methods based on reinforcementlearning offer an appealing choice for such settings, as they can enable robotsto learn to delicately balance contact forces and dexterously repositionobjects without strong modeling assumptions. However, running reinforcementlearning on real-world dexterous manipulation systems often requiressignificant manual engineering. This negates the benefits of autonomous datacollection and ease of use that reinforcement learning should in principleprovide. In this paper, we describe a system for vision-based dexterousmanipulation that provides a "programming-free" approach for users to definenew tasks and enable robots with complex multi-fingered hands to learn toperform them through interaction. The core principle underlying our system isthat, in a vision-based setting, users should be able to provide high-levelintermediate supervision that circumvents challenges in teleoperation orkinesthetic teaching which allow a robot to not only learn a task efficientlybut also to autonomously practice. Our system includes a framework for users todefine a final task and intermediate sub-tasks with image examples, areinforcement learning procedure that learns the task autonomously withoutinterventions, and experimental results with a four-finger robotic handlearning multi-stage object manipulation tasks directly in the real world,without simulation, manual modeling, or reward engineering.

Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance