Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning
This addresses a problem for robotics researchers and developers working on human-in-the-loop systems, but it appears incremental as it builds on a previous study.
The paper tackles the challenge of integrating humans into robotic social navigation tasks, which is incompatible with many state-of-the-art machine learning algorithms like deep reinforcement learning, by analyzing an alternative approach using CNN encoders and incremental learning.
Dealing with social tasks in robotic scenarios is difficult, as having humans in the learning loop is incompatible with most of the state-of-the-art machine learning algorithms. This is the case when exploring Incremental learning models, in particular the ones involving reinforcement learning. In this work, we discuss this problem and possible solutions by analysing a previous study on adaptive convolutional encoders for a social navigation task.