tinyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots

• URL: http://arxiv.org/abs/2608.17596v1
• Date: Tue, 18 Aug 2026 10:03:56 GMT
• Title: tinyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots
• Abstract summary: In this study, we investigate developmental mechanisms that enable small, resource-constrained systems to explore, learn, and adapt their capabilities throughout their lifespan.<n>We strive for minimal, hard-wired skills while encouraging the open-ended development of new skills.<n>In our experiments, we use a resource-constrained millirobot with a volume of 36 cm3 with a Raspberry Pi Pico 32-bit microcontroller.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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 attempts to cover very 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 (RP2040) 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.

Related papers

• Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science [79.50667364149979]
  We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition.<n>The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while switching flexibly between these learning modes as a function of internally generated meta-control signals (System M).
  arXiv  Detail & Related papers  (2026-03-16T14:54:56Z)
• Knowledge-Informed Automatic Feature Extraction via Collaborative Large Language Model Agents [3.913122709822389]
  Rogue One is a novel, multi-agent framework for knowledge-informed automatic feature extraction.<n>We demonstrate that Rogue One significantly outperforms state-of-the-art methods on a comprehensive suite of 19 classification and 9 regression datasets.
  arXiv  Detail & Related papers  (2025-11-19T03:27:14Z)
• Fundamentals of Building Autonomous LLM Agents [64.39018305018904]
  This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs)<n>The research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities.
  arXiv  Detail & Related papers  (2025-10-10T10:32:39Z)
• Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance [81.05882480184587]
  We propose a computational framework of Kolb's learning cycle with Vygotsky's ZPD for autonomous agents.<n>Agent K is the 1st AI system to successfully integrate Kolb- and Vygotsky-inspired human cognitive learning.<n>With 9 gold, 8 silver, and 12 bronze medals level performance - including 4 gold and 4 silver on prize-awarding competitions - Agent K is the 1st AI system to successfully integrate Kolb- and Vygotsky-inspired human cognitive learning.
  arXiv  Detail & Related papers  (2024-11-05T23:55:23Z)
• Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy [31.818923556912495]
  We introduce a new self-supervised neuro-symbolic (NeSy) computational framework, imperative learning (IL) for robot autonomy.<n>We formulate IL as a special bilevel optimization (BLO) which enables reciprocal learning over the three modules.<n>We show that IL can significantly enhance robot autonomy capabilities and we anticipate that it will catalyze further research across diverse domains.
  arXiv  Detail & Related papers  (2024-06-23T12:02:17Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning [114.36124979578896]
  We design a dynamic mechanism using offline reinforcement learning algorithms. Our algorithm is based on the pessimism principle and only requires a mild assumption on the coverage of the offline data set.
  arXiv  Detail & Related papers  (2022-05-05T05:44:26Z)
• Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning [5.478764356647437]
  A novel cognitive architecture for multi-agent LfD robotic learning is introduced, targeting to enable the reliable deployment of open, scalable and expandable robotic systems. The conceptualization relies on employing multiple AI-empowered cognitive processes that operate at the edge nodes of a network of robotic platforms. The applicability of the proposed framework is explained using an example of a real-world industrial case study.
  arXiv  Detail & Related papers  (2020-12-15T09:49:22Z)
• Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems [71.14339738190202]
  democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms.
  arXiv  Detail & Related papers  (2020-07-07T08:34:48Z)
• Uncertainty-based Modulation for Lifelong Learning [1.3334365645271111]
  We present an algorithm inspired by neuromodulatory mechanisms in the human brain that integrates and expands upon Stephen Grossberg's Adaptive Resonance Theory proposals. Specifically, it builds on the concept of uncertainty, and employs a series of neuromodulatory mechanisms to enable continuous learning. We demonstrate the critical role of developing these systems in a closed-loop manner where the environment and the agent's behaviors constrain and guide the learning process.
  arXiv  Detail & Related papers  (2020-01-27T14:34:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.