# Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots

arXiv: https://arxiv.org/abs/2205.05748 · HTML: https://mlsanity.com/paper/2205.05748/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 12 of 12)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://mlsanity.com/paper/1802.05522/) (2018)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Deep Drone Racing: Learning Agile Flight in Dynamic Environments](https://mlsanity.com/paper/1806.08548/) (2018)
- [Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation](https://mlsanity.com/paper/1906.00421/) (2019)
- [Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments](https://mlsanity.com/paper/2107.05490/) (2021)
- [CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs](https://mlsanity.com/paper/2201.01863/) (2022)
- [Placement Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.08445/) (2020)
- [Robot Co-design: Beyond the Monotone Case](https://mlsanity.com/paper/1902.05880/) (2019)

## Built on by (most cited first, 17 of 17)

- [Streaming Deep Reinforcement Learning Finally Works](https://mlsanity.com/paper/2410.14606/) (2024)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers](https://mlsanity.com/paper/2301.11899/) (2023)
- [Latent Distillation for Continual Object Detection at the Edge](https://mlsanity.com/paper/2409.01872/) (2024)
- [Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion](https://mlsanity.com/paper/2210.08065/) (2022)
- [TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection](https://mlsanity.com/paper/2409.16215/) (2024)
- [Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse](https://mlsanity.com/paper/2206.13714/) (2022)
- [Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning](https://mlsanity.com/paper/2310.01767/) (2023)
- [Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medical Diagnosis](https://mlsanity.com/paper/2312.09854/) (2023)
- [Tiny Reinforcement Learning for Quadruped Locomotion using Decision Transformers](https://mlsanity.com/paper/2402.13201/) (2024)
- [Can Optimal Transport Improve Federated Inverse Reinforcement Learning?](https://mlsanity.com/paper/2601.00309/) (2026)
- [OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning](https://mlsanity.com/paper/2401.11652/) (2024)
- [Efficient Morphology-Aware Policy Transfer to New Embodiments](https://mlsanity.com/paper/2508.03660/) (2025)
- [Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/2603.08588/) (2026)
- [Nano-U: Efficient Terrain Segmentation for Tiny Robot Navigation](https://mlsanity.com/paper/2605.10210/) (2026)
- [Minimalist Visual Inertial Odometry](https://mlsanity.com/paper/2605.19990/) (2026)
- [tinyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots](https://mlsanity.com/paper/2608.17596/) (2026)

## Abstract (arXiv)

Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning lies at the intersection of embedded systems, robotics, and ML, compounding the challenges of these domains. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints; sensor, actuator, and compute hardware limitations; end-to-end system tradeoffs; and a large diversity of possible deployment scenarios. Tiny robot learning requires ML models to be designed with these challenges in mind, providing a crucible that reveals the necessity of holistic ML system design and automated end-to-end design tools for agile development. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.