Dexterous manipulation, often facilitated by multi-fingered robotic hands,
holds solid impact for real-world applications. Soft robotic hands, due to
their compliant nature, offer flexibility and adaptability during object
grasping and manipulation. Yet, benefits come with challenges, particularly in
the control development for finger coordination. Reinforcement Learning (RL)
can be employed to train object-specific in-hand manipulation policies, but
limiting adaptability and generalizability. We introduce a Continual Policy
Distillation (CPD) framework to acquire a versatile controller for in-hand
manipulation, to rotate different objects in shape and size within a
four-fingered soft gripper. The framework leverages Policy Distillation (PD) to
transfer knowledge from expert policies to a continually evolving student
policy network. Exemplar-based rehearsal methods are then integrated to
mitigate catastrophic forgetting and enhance generalization. The performance of
the CPD framework over various replay strategies demonstrates its effectiveness
in consolidating knowledge from multiple experts and achieving versatile and
adaptive behaviours for in-hand manipulation tasks.

通过引入 Continual Policy Distillation（CPD）框架，该研究介绍了一种实现多功能控制器的方法，以用于四指软抓手对不同形状和大小的物体进行旋转操作的柔性机器人手。该框架利用策略蒸馏（PD）从专家策略向不断发展的学生策略网络传递知识，并集成样本回放方法以增强泛化能力和避免灾难性遗忘。研究结果表明，CPD 框架在整合多个专家知识和实现多功能自适应手部操作任务方面具有较好的性能。