Employing a teleoperation system for gathering demonstrations offers the
potential for more efficient learning of robot manipulation. However,
teleoperating a robot arm equipped with a dexterous hand or gripper, via a
teleoperation system poses significant challenges due to its high
dimensionality, complex motions, and differences in physiological structure.
In this study, we introduce a novel system for joint learning between human
operators and robots, that enables human operators to share control of a robot
end-effector with a learned assistive agent, facilitating simultaneous human
demonstration collection and robot manipulation teaching. In this setup, as
data accumulates, the assistive agent gradually learns. Consequently, less
human effort and attention are required, enhancing the efficiency of the data
collection process. It also allows the human operator to adjust the control
ratio to achieve a trade-off between manual and automated control.
We conducted experiments in both simulated environments and physical
real-world settings. Through user studies and quantitative evaluations, it is
evident that the proposed system could enhance data collection efficiency and
reduce the need for human adaptation while ensuring the collected data is of
sufficient quality for downstream tasks. Videos are available at
this https URL

通过使用一个新型的人机协作系统，允许人类操作员与机器人共同控制机器人末端执行器，从而实现了同时进行人类示范收集和机器人操作教学，以提高数据收集效率和降低人类适应性需求。

人－机联合学习的高效机器人操作技能获取

Human-Agent Joint Learning for Efficient Robot Manipulation Skill  Acquisition

Vision-based teleoperation offers the possibility to endow robots with
human-level intelligence to physically interact with the environment, while
only requiring low-cost camera sensors. However, current vision-based
teleoperation systems are designed and engineered towards a particular robot
model and deploy environment, which scales poorly as the pool of the robot
models expands and the variety of the operating environment increases. In this
paper, we propose AnyTeleop, a unified and general teleoperation system to
support multiple different arms, hands, realities, and camera configurations
within a single system. Although being designed to provide great flexibility to
the choice of simulators and real hardware, our system can still achieve great
performance. For real-world experiments, AnyTeleop can outperform a previous
system that was designed for a specific robot hardware with a higher success
rate, using the same robot. For teleoperation in simulation, AnyTeleop leads to
better imitation learning performance, compared with a previous system that is
particularly designed for that simulator. Project page: this http URL

提出了一个统一而通用的遥操作系统 AnyTeleop，支持多个不同的机器人模型、手型、现实以及摄像头配置，实现了高性能的真实世界实验和仿真中的模仿学习性能。