Assistive robotic arms often have more degrees-of-freedom than a human
teleoperator can control with a low-dimensional input, like a joystick. To
overcome this challenge, existing approaches use data-driven methods to learn a
mapping from low-dimensional human inputs to high-dimensional robot actions.
However, determining if such a black-box mapping can confidently infer a user's
intended high-dimensional action from low-dimensional inputs remains an open
problem. Our key idea is to adapt the assistive map at training time to
additionally estimate high-dimensional action quantiles, and then calibrate
these quantiles via rigorous uncertainty quantification methods. Specifically,
we leverage adaptive conformal prediction which adjusts the intervals over
time, reducing the uncertainty bounds when the mapping is performant and
increasing the bounds when the mapping consistently mis-predicts. Furthermore,
we propose an uncertainty-interval-based mechanism for detecting
high-uncertainty user inputs and robot states. We evaluate the efficacy of our
proposed approach in a 2D assistive navigation task and two 7DOF Kinova Jaco
tasks involving assistive cup grasping and goal reaching. Our findings
demonstrate that conformalized assistive teleoperation manages to detect (but
not differentiate between) high uncertainty induced by diverse preferences and
induced by low-precision trajectories in the mapping's training dataset. On the
whole, we see this work as a key step towards enabling robots to quantify their
own uncertainty and proactively seek intervention when needed.

通过统计学方法和数据驱动的模型，我们提出了一种辅助机器人操作的方法，可以准确地估计用户意图，并主动寻求干预。

合拢式远程操作：自信地将人类输入映射为高维机器人动作

Conformalized Teleoperation: Confidently Mapping Human Inputs to  High-Dimensional Robot Actions

Assistive technologies and in particular assistive robotic arms have the
potential to enable people with motor impairments to live a self-determined
life. More and more of these systems have become available for end users in
recent years, such as the Kinova Jaco robotic arm. However, they mostly require
complex manual control, which can overwhelm users. As a result, researchers
have explored ways to let such robots act autonomously. However, at least for
this specific group of users, such an approach has shown to be futile. Here,
users want to stay in control to achieve a higher level of personal autonomy,
to which an autonomous robot runs counter. In our research, we explore how
Artifical Intelligence (AI) can be integrated into a shared control paradigm.
In particular, we focus on the consequential requirements for the interface
between human and robot and how we can keep humans in the loop while still
significantly reducing the mental load and required motor skills.

论文探讨了如何将人工智能集成到共享控制范式中，特别关注了人与机器人之间接口的要求和如何在显著减少心理负担和所需的运动技能的同时保持人的参与。