A Dynamic Movement Primitives-Based Tool Use Skill Learning and Transfer Framework for Robot Manipulation

This paper presents a framework for learning and transferring robot tool-use skills based on Dynamic Movement Primitives (DMPs) for robot fine manipulation. DMPs and their enhanced methods are employed to acquire a specific tool-use skill applicable to tools with similar sizes, shapes, and uses. However, the acquired skills may not be transferable to other scenarios and tools with variations. The new framework introduces two new types of skills based on DMPs: Object Operating (O2) skill and Tool Flipping (TF) skill. The O2 skill enables robots to handle tools for manipulating objects to achieve desired effects. The learning process for the O2 skill considers limitations imposed by tools and the environment during human demonstrations. Distinguishing between whether constraints can be modelled or not, we propose both a model-based and a constraint-based method to separate a constraint-irrelevant (CI) skill and the constrained conditions. The CI skill is generalized using a novel method called constrained -DMP lite, enabling adaptation to new tasks with special tools. The TF skill addresses situations where tools must generate an action to alter contacting positions on both objects and tools while avoiding conflicts during movement. Finally, the TF and O2 skills are generalized to be applied in creating a continuous action chain. We conduct several experiments to compare and analyze the advantages and disadvantages of the proposed methods with other approaches in terms of generalizability and calculation complexity. Note to Practitioners-Strengthening robot tool-use ability has been a hot research topic in recent years because these tools can extend the reachability and enhance the flexibility of robots. The previous research on DMPs has been utilized for learning tool-use skills. However, the learned skills few considered the tools' special use regulations, therefore the skill of using a tool is hard to transfer to another tool-use case. This paper explores tool-use skill learning and transfer between different tools by developing a framework based on the DMPs for this problem. The framework consists of two kinds of skills: O2 skill and TF skill with different purposes as well as a series of newly developed algorithms, such as constrained -DMP lite, a model-based and a constraint-based CI skill learning methods. These methods can separate the constraints from human demonstrations of using tools to achieve a CI skill and generalize the CI skill according to the constraints generated from a new tool-use manipulation task. We verify the effectiveness of the proposed framework through some typical tool-use experiments, including pushing objects, cutting and obstacle avoidance in actuality. The development of this framework can be used in industrial and house working scenarios.