In this research, we introduce a deep reinforcement learning-based control
approach to address the intricate challenge of the robotic pre-grasping phase
under microgravity conditions. Leveraging reinforcement learning eliminates the
necessity for manual feature design, therefore simplifying the problem and
empowering the robot to learn pre-grasping policies through trial and error.
Our methodology incorporates an off-policy reinforcement learning framework,
employing the soft actor-critic technique to enable the gripper to proficiently
approach a free-floating moving object, ensuring optimal pre-grasp success. For
effective learning of the pre-grasping approach task, we developed a reward
function that offers the agent clear and insightful feedback. Our case study
examines a pre-grasping task where a Robotiq 3F gripper is required to navigate
towards a free-floating moving target, pursue it, and subsequently position
itself at the desired pre-grasp location. We assessed our approach through a
series of experiments in both simulated and real-world environments. The source
code, along with recordings of real-world robot grasping, is available at
Fanuc_Robotiq_Grasp.

利用深度强化学习的控制方法，在微重力条件下解决机器人的抓取前阶段的复杂挑战，通过试错学习，消除了手动设计特征的必要性，使机器人能够学习抓取策略。

走向实际效率：自主机器人在自由漂移移动目标的预捕获中的强化学习中领域随机化

Towards Real-World Efficiency: Domain Randomization in Reinforcement  Learning for Pre-Capture of Free-Floating Moving Targets by Autonomous Robots

We present SpaceAgents-1, a system for learning human and multi-robot
collaboration (HMRC) strategies under microgravity conditions. Future space
exploration requires humans to work together with robots. However, acquiring
proficient robot skills and adept collaboration under microgravity conditions
poses significant challenges within ground laboratories. To address this issue,
we develop a microgravity simulation environment and present three typical
configurations of intra-cabin robots. We propose a hierarchical heterogeneous
multi-agent collaboration architecture: guided by foundation models, a
Decision-Making Agent serves as a task planner for human-robot collaboration,
while individual Skill-Expert Agents manage the embodied control of robots.
This mechanism empowers the SpaceAgents-1 system to execute a range of
intricate long-horizon HMRC tasks.

SpaceAgents-1 系统在微重力环境下学习人机和多机器人协作策略，通过开发微重力模拟环境和引入分层异构多机器人协作架构，解决了地面实验室中获得熟练机器人技能和在微重力条件下进行高效协作的难题。

我们选择去太空：人与多机器人在失重状态下的代理驱动协作

We Choose to Go to Space: Agent-driven Human and Multi-Robot  Collaboration in Microgravity

Bone degradation, especially for astronauts in microgravity conditions, is
crucial for space exploration missions since the lower applied external forces
accelerate the diminution in bone stiffness and strength substantially.
Although existing computational models help us understand this phenomenon and
possibly restrict its effect in the future, they are time-consuming to simulate
the changes in the bones, not just the bone microstructures, of each individual
in detail. In this study, a robust yet fast computational method to predict and
visualize bone degradation has been developed. Our deep-learning method,
TransVNet, can take in different 3D voxelized images and predict their
evolution throughout months utilizing a hybrid 3D-CNN-VisionTransformer
autoencoder architecture. Because of limited available experimental data and
challenges of obtaining new samples, a digital twin dataset of diverse and
initial bone-like microstructures was generated to train our TransVNet on the
evolution of the 3D images through a previously developed degradation model for
microgravity.

通过利用混合的 3D-CNN-VisionTransformer 自编码器架构，我们开发了一种强大且快速的深度学习方法 TransVNet，可以预测和可视化骨骼退化的演变，并提出了一个数字孪生数据集用于训练。