tinyDSM：面向资源受限毫米级机器人的技能建模与开发框架
tinyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots
- Institute of Computer Technology, TU Wien（维也纳工业大学计算机技术研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出面向资源受限毫米级机器人的 tinyDSM 框架，结合内在动机与适应性评估，依托分层知识图谱等实现技能建模，使机器人15分钟内自主掌握从基础到复杂的运动技能。
AI中文摘要：
本研究探讨使厘米级毫米级机器人等小型资源受限系统在整个生命周期内自主探索、学习并调整自身能力的发育机制。强化学习算法通过我们提出的 tinyDSM 实现智能体的技能获取与调整，tinyDSM 整合了内在动机与基于适应性的评估。我们追求最小化的硬连线技能，同时鼓励新技能的开放式开发。本方法的一个核心重点是编码最少的先验通用知识，这作为系统的基础起点，使其能从提供的初始知识中进一步学习特定于系统的依赖关系。因此，本方法旨在覆盖非常通用的应用领域。该方法基于：(a) 具有内在动机的发育机制，(b) 认知架构（知识、推理、学习），同时 (c) 利用最少的资源。它使用分层知识图谱和运动学推理器来建模和评估与简单及高级运动相关的技能。在实验中，我们使用体积为 36 cm^3 的资源受限毫米级机器人，其搭载 Raspberry Pi Pico 32 位微控制器（RP2040），除相机系统外，所有所述功能和能力均集成在 9 kB 的存储空间中。从学习最基础的运动技能开始，该毫米级机器人在 15 分钟内自主从简单的线性和角运动进展到复杂的几何图案。为补充物理实验，我们进行了基于模拟的分析，该分析可实现对学习算法和内在动机参数的系统比较。
英文摘要：
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 it further learns system-specific dependencies from the initial knowledge provided. Thus, by design, our approach aims to cover generic application domains. The methodology is based on (a) developmental mechanism with intrinsic motivation, and (b) a cognitive architecture (knowledge, reasoning, learning), while (c) utilizing minimal resources. It uses a hierarchical knowledge graph and kinematic reasoners to model and evaluate simple and advanced motion related skills. In our experiments, we use a resource-constrained millirobot with a volume of 36 cm^3 with a Raspberry Pi Pico 32-bit microcontroller that integrates all described features and capabilities except the camera system in 9 kB. Starting with learning the most elementary motor skills the millirobot autonomously progresses from simple linear and angular movements to complex geometric patterns within 15 minutes. To complement the physical experiments, we perform a simulation-based analysis that enables systematic comparisons across learning algorithms and intrinsic motivation parameters.
