Learning to Represent Surroundings, Anticipate Motion and Take Informed Actions in Unstructured Environments

TL;DR

This paper introduces learning-based methods that enable robots to understand unstructured environments, predict future actions, and make informed decisions, addressing the limitations of traditional rule-based approaches in complex real-world settings.

Contribution

It presents novel learning algorithms for robots to adaptively perceive, anticipate, and act in dynamic, unstructured environments, improving their autonomy and robustness.

Findings

Enhanced environmental understanding in robots

Improved prediction of other agents' actions

More informed and adaptive robot behaviors

Abstract

Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly.