1.Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization
Sim2Real Transfer用于无需动态随机化的强化学习
2.Combining Compliance Control, CAD Based Localization, and a Multi-Modal Gripper for Rapid and Robust Programming of Assembly Tasks
结合了顺应性控制，基于CAD的本地化和多模式抓取器，可对装配任务进行快速而鲁棒的编程
3.Model-Free, Vision-Based Object Identification and Contact Force Estimation with a Hyper-Adaptive Robotic Gripper
基于超自适应机器人抓手的无模型，基于视觉的对象识别和接触力估计
4.KOVIS: Keypoint-Based Visual Servoing with Zero-Shot Sim-To-Real Transfer for Robotics Manipulation
KOVIS：基于关键点的视觉伺服系统，采用零射模拟到真实的转移进行机器人操纵
5.Assisted Mobile Robot Teleoperation with Intent-Aligned Trajectories Via Biased Incremental Action Sampling
通过偏移增量动作采样实现意图对准轨迹的辅助移动机器人遥操作
6.Variable In-Hand Manipulations for Tactile-Driven Robot Hand Via CNN-LSTM
通过CNN-LSTM进行触觉驱动的机器人手的可变手操作
7.Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
用于工业任务的以对象为中心的操作技能的学习和排序
8.Sample-Efficient Learning for Industrial Assembly Using Qgraph-Bounded DDPG
使用Qgraph绑定DDPG进行工业装配的高效样本学习
9.A Learning-based Robotic Bin-picking with Flexibly Customizable Grasping Conditions
具有灵活自定义抓取条件的基于学习的机器人装箱
10.Virtual Reality for Robots
机器人的虚拟现实
11.
原文：https://www.cnblogs.com/feifanrensheng/p/14169578.html