论文标题
了解有效工具使用的物理效果
Understanding Physical Effects for Effective Tool-use
论文作者
论文摘要
我们提出了一个机器人学习和计划框架，该框架以最少的共同努力生成有效的工具使用策略，能够处理不同于培训的对象。利用有限元方法（FEM）基于模拟器，它可以通过观察到的工具使用事件重现细粒度的，连续的视觉和物理效果，通过提出的迭代迭代符号深化符号回归（IDSR）算法确定了促成效果的基本物理特性。我们进一步设计了一种基于最佳控制的运动计划方案，以整合机器人和特定于工具的运动学和动力学，以产生有效的轨迹，从而构成学习性能。在模拟中，我们证明了所提出的框架可以产生更有效的工具使用策略，这与在两个示例任务中观察到的框架截然不同。
We present a robot learning and planning framework that produces an effective tool-use strategy with the least joint efforts, capable of handling objects different from training. Leveraging a Finite Element Method (FEM)-based simulator that reproduces fine-grained, continuous visual and physical effects given observed tool-use events, the essential physical properties contributing to the effects are identified through the proposed Iterative Deepening Symbolic Regression (IDSR) algorithm. We further devise an optimal control-based motion planning scheme to integrate robot- and tool-specific kinematics and dynamics to produce an effective trajectory that enacts the learned properties. In simulation, we demonstrate that the proposed framework can produce more effective tool-use strategies, drastically different from the observed ones in two exemplar tasks.
