tinyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots
In this study, we investigate developmental mechanisms that enable small, resource-constrained systems such as cm-sized millirobots to autonomously explore, learn, and adapt their capabilities throughout their lifespan. Reinforcement learning algorithms guide the agent's skill acquisition and adaptation through the interplay of our proposed tiny Developmental Skill Method (tinyDSM), which integrates intrinsic motivation and fitness-based assessment. We strive for minimal hard-wired skills while encouraging the open-ended development of new skills. A key emphasis in our approach is to encode minimal a-priori general knowledge, which serves as a foundational starting point for the system as...
| Authors | Markus Kobelrausch, Michael Miedler, Axel Jantsch |
|---|---|
| Primary category | cs.RO |
| Published | 18 August 2026 |
| Last updated at source | 9 September 2026 |
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