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

We consider partially-connected $K$-user interference networks, where the
transmitters have no knowledge about the channel gain values, but they are
aware of network topology (or connectivity). We introduce several linear
algebraic and graph theoretic concepts to derive new topology-based outer
bounds and inner bounds on the symmetric degrees-of-freedom (DoF) of these
networks. We evaluate our bounds for two classes of networks to demonstrate
their tightness for most networks in these classes, quantify the gain of our
inner bounds over benchmark interference management strategies, and illustrate
the effect of network topology on these gains.

该研究讨论了利用线性代数和图论概念来推导网络容量的新界内界，证明了该方法在几乎所有网络中的优越性。