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。

基于强化学习的软体机械手控制器的持续策略蒸馏

Continual Policy Distillation of Reinforcement Learning-based  Controllers for Soft Robotic In-Hand Manipulation

Robotic grasping is a fundamental skill required for object manipulation in
robotics. Multi-fingered robotic hands, which mimic the structure of the human
hand, can potentially perform complex object manipulations. Nevertheless,
current techniques for multi-fingered robotic grasping frequently predict only
a single grasp for each inference time, limiting their versatility and
efficiency. This paper proposes a differentiable multi-fingered grasp
generation network (DMFC-GraspNet) with two main contributions to address this
challenge. Firstly, a novel neural grasp planner is proposed, which predicts a
new grasp representation to enable versatile and dense grasp predictions.
Secondly, a scene creation and label mapping method is developed for dense
labeling of multi-fingered robotic hands, which allows a dense association of
ground truth grasps. The proposed approach is evaluated through simulation
studies and compared to existing approaches. The results demonstrate the
effectiveness of the proposed approach in predicting versatile and dense
grasps, and in advancing the field of robotic grasping.

本论文提出了一种可微分的多指抓取生成网络（DMFC-GraspNet），通过引入新的抓取表示和密集标签映射方法，使得多指机器人抓取更加灵活、密集，推动了机器人抓取领域的发展。

DMFC-GraspNet：多指可微分机器人抓取在杂乱场景中的生成

DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in  Cluttered Scenes

Dexterous multi-fingered robotic hands can perform a wide range of
manipulation skills, making them an appealing component for general-purpose
robotic manipulators. However, such hands pose a major challenge for autonomous
control, due to the high dimensionality of their configuration space and
complex intermittent contact interactions. In this work, we propose deep
reinforcement learning (deep RL) as a scalable solution for learning complex,
contact rich behaviors with multi-fingered hands. Deep RL provides an
end-to-end approach to directly map sensor readings to actions, without the
need for task specific models or policy classes. We show that contact-rich
manipulation behavior with multi-fingered hands can be learned by directly
training with model-free deep RL algorithms in the real world, with minimal
additional assumption and without the aid of simulation. We learn a variety of
complex behaviors on two different low-cost hardware platforms. We show that
each task can be learned entirely from scratch, and further study how the
learning process can be further accelerated by using a small number of human
demonstrations to bootstrap learning. Our experiments demonstrate that complex
multi-fingered manipulation skills can be learned in the real world in about
4-7 hours for most tasks, and that demonstrations can decrease this to 2-3
hours, indicating that direct deep RL training in the real world is a viable
and practical alternative to simulation and model-based control.
https://sites.google.com/view/deeprl-handmanipulation

本文提出使用深度强化学习作为一种可扩展的解决方案，来掌握带有多指手的接触丰富行为，并展示使用模型自由的深度强化学习算法在现实世界中可以学习各种复杂的行为，同时可以通过少量的人类演示来加速学习。