This thesis explores the use of reinforcement learning within the Unity ML Agents framework to simulate zombie-like movements in humanoid ragdolls. The generated locomotion aims to embrace the Uncanny Valley phenomenon, partly through the way it walks, but also through limb disablement. Additionally, the paper strives to test the effectiveness of reinforcement learning as a valuable tool for generative adaptive locomotion. The research implements reward functions and addresses technical challenges. It lays a focus on adaptability through the limb disablement system. A user study comparing the reinforcement learning agent to Mixamo animations evaluates the effectiveness of simulating zombie-like movements as well as if the Uncanny Valley phenomenon was achieved. Results show that while the reinforcement learning agent may lack believability and uncanniness when compared to the Mixamo animation, it features a level of adaptability that is worth expanding upon. Given the inconclusive results, there is room for further research on the topic to achieve the Uncanny Valley effect and enhance zombie-like locomotion with reinforcement learning.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-68996 |
| Date | January 2024 |
| Creators | Tayeh, Adrian, Almquist, Arvid |
| Publisher | Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0834 seconds