  The capability to transfer mastered skills to accomplish a range of similar
yet novel tasks is crucial for intelligent robots. In this work, we introduce
$\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics
simulation to efficiently transfer robotic skills. Specifically,
$\textit{Diff-Transfer}$ discovers a feasible path within the task space that
brings the source task to the target task. At each pair of adjacent points
along this task path, which is two sub-tasks, $\textit{Diff-Transfer}$ adapts
known actions from one sub-task to tackle the other sub-task successfully. The
adaptation is guided by the gradient information from differentiable physics
simulations. We propose a novel path-planning method to generate sub-tasks,
leveraging $Q$-learning with a task-level state and reward. We implement our
framework in simulation experiments and execute four challenging transfer tasks
on robotic manipulation, demonstrating the efficacy of $\textit{Diff-Transfer}$
through comprehensive experiments. Supplementary and Videos are on the website
https://sites.google.com/view/difftransfer
