Reducing risk for assistive reinforcement learning policies with diffusion models

Tytarenko,  Andrii

Reducing risk for assistive reinforcement learning policies with diffusion models

Файли

315284-730152-1-10-20241116.pdf (251.25 KB)

Дата

2024

Автори

Tytarenko, Andrii

Видавець

КПІ ім. Ігоря Сікорського

Анотація

Care-giving and assistive robotics, driven by advancements in AI, offer promising solutions to meet the growing demand for care, particularly in the context of increasing numbers of individuals requiring assistance. It creates a pressing need for efficient and safe assistive devices, particularly in light of heightened demand due to war-related injuries. While cost has been a barrier to accessibility, techno-logical progress can democratize these solutions. Safety remains a paramount con-cern, especially given the intricate interactions between assistive robots and humans. This study explores the application of reinforcement learning (RL) and imitation learning in improving policy design for assistive robots. The proposed approach makes the risky policies safer without additional environmental interactions. The enhancement of the conventional RL approaches in tasks related to assistive robotics is demonstrated through experimentation using simulated environments.

Ключові слова

assistive robotics, reinforcement learning, diffusion models, imitation learning, допоміжна робототехніка, навчання з підкріпленням, дифузійні моделі, імітаційне навчання

Бібліографічний опис

Tytarenko, A. Reducing risk for assistive reinforcement learning policies with diffusion models / A. Tytarenko // Системні дослідження та інформаційні технології : міжнародний науково-технічний журнал. – 2024. – № 3. – С. 148-154 . – Бібліогр.: 12 назв.