Robot skill learning and execution in uncertain and dynamic environments is a challenging task. This paper proposes an adaptive framework that combines Learning from Demonstration (LfD), environment state prediction, and high-level decision making. Proactive adaptation prevents the need for reactive adaptation, which lags behind changes in the environment rather than anticipating them. We propose a novel LfD representation, Elastic-Laplacian Trajectory Editing (ELTE), which continuously adapts the trajectory shape to predictions of future states. Then, a high-level reactive system using an Unscented Kalman Filter (UKF) and Hidden Markov Model (HMM) prevents unsafe execution in the current state of the dynamic environment based on a discrete set of decisions. We first validate our LfD representation in simulation, then experimentally assess the entire framework using a legged mobile manipulator in 36 real-world scenarios. We show the effectiveness of the proposed framework under different dynamic changes in the environment. Our results show that the proposed framework produces robust and stable adaptive behaviors.
@inproceedings{donald2024adaptive,title={An Adaptive Framework for Manipulator Skill Reproduction in Dynamic Environments},author={Donald, Ryan and Hertel, Brendan and Misenti, Stephen and Yan, Gao and Azadeh, Reza},booktitle={2024 21st International Conference on Ubiquitous Robots (UR)},year={2024},month=jun,}
2023
ICARA
Contextual Autonomy Evaluation of Unmanned Aerial Vehicles in Subterranean Environments
Ryan Donald, Peter Gavriel, Adam Norton, and 1 more author
In 2023 9th International Conference on Automation, Robotics and Applications (ICARA), Feb 2023
In this paper we focus on the evaluation of contextual autonomy for robots. More specifically, we propose a fuzzy framework for calculating the autonomy score for a small Unmanned Aerial Systems (sUAS) for performing a task while considering task complexity and environmental factors. Our framework is a cascaded Fuzzy Inference System (cFIS) composed of combination of three FIS which represent different contextual autonomy capabilities. We performed several experiments to test our framework in various contexts, such as endurance time, navigation, take off/land, and room clearing, with seven different sUAS. We introduce a predictive measure which improves upon previous predictive measures, allowing for previous real-world task performance to be used in predicting future mission performance.
@inproceedings{donald2023contextual,title={Contextual Autonomy Evaluation of Unmanned Aerial Vehicles in Subterranean Environments},author={Donald, Ryan and Gavriel, Peter and Norton, Adam and Ahmadzadeh, S. Reza},booktitle={2023 9th International Conference on Automation, Robotics and Applications (ICARA)},year={2023},month=feb,}
2022
ICARA
Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems
Brendan Hertel, Ryan Donald, Christian Dumas, and 1 more author
In 2022 8th International Conference on Automation, Robotics and Applications (ICARA), Feb 2022
Measuring an overall autonomy score for a robotic system requires the combination of a set of relevant aspects and features of the system that might be measured in different units, qualitative, and/or discordant. In this paper, we build upon an existing non-contextual autonomy framework that measures and combines the Autonomy Level and the Component Performance of a system as overall autonomy score. We examine several methods of combining features, showing how some methods find different rankings of the same data, and we employ the weighted product method to resolve this issue. Furthermore, we introduce the non-contextual autonomy coordinate and represent the overall autonomy of a system with an autonomy distance. We apply our method to a set of seven Unmanned Aerial Systems (UAS) and obtain their absolute autonomy score as well as their relative score with respect to the best system.
@inproceedings{hertel2022methods,title={Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems},author={Hertel, Brendan and Donald, Ryan and Dumas, Christian and Ahmadzadeh, S. Reza},booktitle={2022 8th International Conference on Automation, Robotics and Applications (ICARA)},year={2022},month=feb,}