In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain types in the environment. These weights are then combined into a costmap, which is passed to a planner for path generation and navigation to a goal.
This thesis extends prior research in learning terrain costs for robot navigation using an active rewards learning Python package, APReL. The novel contribution of this thesis is that the robot not only considers traversability affordances of different terrains for navigation, but also considers concealment from an adversary in the environment. This research work also augments the APReL package with wrappers suitable for the use-case. The model will be evaluated by comparing its performance with an already existing Inverse Optimal Control (IOC) model trained from human demonstrations as a baseline learning algorithm for autonomous robot navigation.
","abstract_html":"<p>In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain types in the environment. These weights are then combined into a costmap, which is passed to a planner for path generation and navigation to a goal.</p> <p>This thesis extends prior research in learning terrain costs for robot navigation using an active rewards learning Python package, APReL. The novel contribution of this thesis is that the robot not only considers traversability affordances of different terrains for navigation, but also considers concealment from an adversary in the environment. This research work also augments the APReL package with wrappers suitable for the use-case. The model will be evaluated by comparing its performance with an already existing Inverse Optimal Control (IOC) model trained from human demonstrations as a baseline learning algorithm for autonomous robot navigation.</p>","abstract_has_math":false,"creators":["Inyang, Aniekan Ufot"],"institution":null,"degree_name":"M.S.","degree_level":"Masters Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Christopher Reardon","Daniel Baack","Maggie Wigness","Matthew Rutherford"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08-01T07:00:00Z","date_published":"2023-08-01T07:00:00Z","updated_at":"2026-07-24T02:01:39Z","subjects":["Adversary aware navigation","Autonomous robot navigation","Context aware navigation","Preference based learning","Robot learning","Terrain aware navigation","Artificial Intelligence and Robotics","Computer Engineering","Computer Sciences","Engineering","Physical Sciences and Mathematics","Robotics"],"languages":["English (eng)"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.du.edu/etd/2299","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Christopher Reardon","Daniel Baack","Maggie Wigness","Matthew Rutherford"]},{"key":"dc:creator","label":"Author","values":["Inyang, Aniekan Ufot"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-09-12T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Adversary aware navigation","Autonomous robot navigation","Context aware navigation","Preference based learning","Robot learning","Terrain aware navigation","Artificial Intelligence and Robotics","Computer Engineering","Computer Sciences","Engineering","Physical Sciences and Mathematics","Robotics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (eng)"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright is held by the author. User is responsible for all copyright compliance.
"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.du.edu/etd/2299"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain types in the environment. These weights are then combined into a costmap, which is passed to a planner for path generation and navigation to a goal.
This thesis extends prior research in learning terrain costs for robot navigation using an active rewards learning Python package, APReL. The novel contribution of this thesis is that the robot not only considers traversability affordances of different terrains for navigation, but also considers concealment from an adversary in the environment. This research work also augments the APReL package with wrappers suitable for the use-case. The model will be evaluated by comparing its performance with an already existing Inverse Optimal Control (IOC) model trained from human demonstrations as a baseline learning algorithm for autonomous robot navigation.
"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Terrain and Adversary-Aware Autonomous Robot Navigation"]}]}],"canonical_facts":{"dc:contributor":["Christopher Reardon","Daniel Baack","Maggie Wigness","Matthew Rutherford"],"dc:creator":["Inyang, Aniekan Ufot"],"dc:date.available":["2024-09-12T07:00:00Z"],"dc:description.abstract":["In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain types in the environment. These weights are then combined into a costmap, which is passed to a planner for path generation and navigation to a goal.
This thesis extends prior research in learning terrain costs for robot navigation using an active rewards learning Python package, APReL. The novel contribution of this thesis is that the robot not only considers traversability affordances of different terrains for navigation, but also considers concealment from an adversary in the environment. This research work also augments the APReL package with wrappers suitable for the use-case. The model will be evaluated by comparing its performance with an already existing Inverse Optimal Control (IOC) model trained from human demonstrations as a baseline learning algorithm for autonomous robot navigation.
"],"dc:format":["application/pdf"],"dc:identifier":["https://digitalcommons.du.edu/etd/2299"],"dc:language":["English (eng)"],"dc:rights":["Copyright is held by the author. User is responsible for all copyright compliance.
"],"dc:subject":["Adversary aware navigation","Autonomous robot navigation","Context aware navigation","Preference based learning","Robot learning","Terrain aware navigation","Artificial Intelligence and Robotics","Computer Engineering","Computer Sciences","Engineering","Physical Sciences and Mathematics","Robotics"],"dc:title":["Terrain and Adversary-Aware Autonomous Robot Navigation"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T02:01:39Z"}