We investigated the application of haptic feedback control and deep
reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to
simultaneously train human and robot control policies as separate neural
networks using physics simulations. In addition, we modeled variations in human
impairments relevant to dressing, including unilateral muscle weakness,
involuntary arm motion, and limited range of motion. Our approach resulted in
control policies that successfully collaborate in a variety of simulated
dressing tasks involving a hospital gown and a T-shirt. In addition, our
approach resulted in policies trained in simulation that enabled a real PR2
robot to dress the arm of a humanoid robot with a hospital gown. We found that
training policies for specific impairments dramatically improved performance;
that controller execution speed could be scaled after training to reduce the
robot's speed without steep reductions in performance; that curriculum learning
could be used to lower applied forces; and that multi-modal sensing, including
a simulated capacitive sensor, improved performance.

本文探讨了触觉反馈控制和深度强化学习在机器人辅助穿衣方面的应用，使用物理仿真同时训练人类和机器人控制政策，建立包括人体不足等障碍模型，并成功地控制机器人进行模拟穿衣。

机器人辅助穿衣任务的协作学习

Learning to Collaborate from Simulation for Robot-Assisted Dressing

Robot-assisted dressing offers an opportunity to benefit the lives of many
people with disabilities, such as some older adults. However, robots currently
lack common sense about the physical implications of their actions on people.
The physical implications of dressing are complicated by non-rigid garments,
which can result in a robot indirectly applying high forces to a person's body.
We present a deep recurrent model that, when given a proposed action by the
robot, predicts the forces a garment will apply to a person's body. We also
show that a robot can provide better dressing assistance by using this model
with model predictive control. The predictions made by our model only use
haptic and kinematic observations from the robot's end effector, which are
readily attainable. Collecting training data from real world physical
human-robot interaction can be time consuming, costly, and put people at risk.
Instead, we train our predictive model using data collected in an entirely
self-supervised fashion from a physics-based simulation. We evaluated our
approach with a PR2 robot that attempted to pull a hospital gown onto the arms
of 10 human participants. With a 0.2s prediction horizon, our controller
succeeded at high rates and lowered applied force while navigating the garment
around a persons fist and elbow without getting caught. Shorter prediction
horizons resulted in significantly reduced performance with the sleeve catching
on the participants' fists and elbows, demonstrating the value of our model's
predictions. These behaviors of mitigating catches emerged from our deep
predictive model and the controller objective function, which primarily
penalizes high forces.

介绍了一种深度循环预测模型，可预测机器人给予人体的服装施加的力量，该模型使用从模拟环境中收集的数据进行自我监督训练，为机器人的穿衣辅助提供了更好的解决方案。