Abstract
We describe a work-in-progress approach to solving the problem of robot navigation in dynamically changing, social environments. Our approach employs reinforcement learning informed by a continually updated model that predicts the evolution of the environment, and handles two common scenarios: (1) a person moving within the environment, and (2) static obstacles with positions that change over time. We assess the effectiveness of the approach in a simulated assistive-care application in which a mobile robot supports a person with mild cognitive or physical impairments with simple everyday tasks.
Original language | English |
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Title of host publication | 24th Towards Autonomous Robotic Systems Conference (TAROS), Proceedings |
Publisher | Springer |
Number of pages | 5 |
Publication status | Published - 15 Sept 2023 |
Event | 24th Towards Autonomous Robotic Systems Conference (TAROS) - Cambridge, United Kingdom Duration: 13 Sept 2023 → 15 Sept 2023 https://taros-conference.org/ |
Conference
Conference | 24th Towards Autonomous Robotic Systems Conference (TAROS) |
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Abbreviated title | TAROS |
Country/Territory | United Kingdom |
City | Cambridge |
Period | 13/09/23 → 15/09/23 |
Internet address |
Bibliographical note
This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.Keywords
- Reinforcement Learning
- Navigation
- Self-Adaptive Systems
- Assistive-Care Robots