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

Recreating complex, high-dimensional global fields from limited data points
is a grand challenge across various scientific and industrial domains. Given
the prohibitive costs of specialized sensors and the frequent inaccessibility
of certain regions of the domain, achieving full field coverage is typically
not feasible. Therefore, the development of algorithms that intelligently
improve sensor placement is of significant value. In this study, we introduce a
general approach that employs differentiable programming to exploit sensor
placement within the training of a neural network model in order to improve
field reconstruction. We evaluated our method using two distinct datasets; the
results show that our approach improved test scores. Ultimately, our method of
differentiable placement strategies has the potential to significantly increase
data collection efficiency, enable more thorough area coverage, and reduce
redundancy in sensor deployment.

通过差分编程在神经网络模型的训练中利用传感器放置来改善场重建，我们的方法有潜力显著提高数据收集效率，实现更全面的区域覆盖，并减少传感器部署的冗余。

稀疏感知中的场重构：可微传感器布置增强泛化能力

Reconstruction of Fields from Sparse Sensing: Differentiable Sensor  Placement Enhances Generalization

In this paper, we aim to explore the use of uplink semantic communications
with the assistance of UAV in order to improve data collection effiicency for
metaverse users in remote areas. To reduce the time for uplink data collection
while balancing the trade-off between reconstruction quality and computational
energy cost, we propose a hybrid action reinforcement learning (RL) framework
to make decisions on semantic model scale, channel allocation, transmission
power, and UAV trajectory. The variables are classified into discrete type and
continuous type, which are optimized by two different RL agents to generate the
combined action. Simulation results indicate that the proposed hybrid action
reinforcement learning framework can effectively improve the efficiency of
uplink semantic data collection under different parameter settings and
outperforms the benchmark scenarios.

本研究旨在探索使用助理无人机的上行语义通信，以提高偏远地区元宇宙用户的数据采集效率。为了在重建质量和计算能量成本之间平衡的同时减少上行数据采集时间，我们提出了一种混合动作强化学习框架，用于在语义模型规模、信道分配、传输功率和无人机轨迹上做出决策。变量分为离散类型和连续类型，并由两个不同的强化学习代理进行优化以生成合并的动作。模拟结果表明，所提出的混合动作强化学习框架可以在不同的参数设置下有效提高上行语义数据采集的效率，并优于基准情况。