Improving Assistive Robotics with Deep Reinforcement Learning
This work addresses the challenge of generalizing assistive robotics for aiding humans with disabilities or age-related limitations, but it is incremental as it builds on existing methods without achieving significant improvements.
The paper tackled the problem of generalizing assistive robotics tasks by replicating a baseline in the Assistive Gym environment and exploring Recurrent Neural Networks and Phasic Policy Gradient learning, but found that the new methods were less effective than anticipated, with the baseline meeting or exceeding original performance.
Assistive Robotics is a class of robotics concerned with aiding humans in daily care tasks that they may be inhibited from doing due to disabilities or age. While research has demonstrated that classical control methods can be used to design policies to complete these tasks, these methods can be difficult to generalize to a variety of instantiations of a task. Reinforcement learning can provide a solution to this issue, wherein robots are trained in simulation and their policies are transferred to real-world machines. In this work, we replicate a published baseline for training robots on three tasks in the Assistive Gym environment, and we explore the usage of a Recurrent Neural Network and Phasic Policy Gradient learning to augment the original work. Our baseline implementation meets or exceeds the baseline of the original work, however, we found that our explorations into the new methods was not as effective as we anticipated. We discuss the results of our baseline and some thoughts on why our new methods were not as successful.