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.

翻译：我们提出了一个机器人学习和规划框架,它能以最少的联合努力产生有效的工具使用战略,能够处理不同于培训的物体。利用一个基于有限元素法的模拟器,在观测到的工具使用事件中,产生精细的、连续的视觉和物理效应,通过拟议的迭代深深显性象征回归算法确定促成这些效应的基本物理特性。我们进一步设计一个基于控制的最佳运动规划计划,将机器人和特定工具的运动和动态结合起来,以产生一种有效的轨迹,释放出所学的特性。在模拟中,我们证明拟议的框架能够产生更有效的工具使用战略,与在两项特有任务中观察到的战略大不相同。