[Paper Review] Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots

Sabrina M. Neuman, Brian Plancher+9 more|arXiv (Cornell University)|May 11, 2022

Modular Robots and Swarm Intelligence4 citations

TL;DR

This paper identifies the challenges of deploying machine learning on resource-constrained, low-cost autonomous robots—termed 'tiny robot learning'—and proposes holistic ML system design and automated end-to-end design tools to address SWAP (size, weight, power) constraints, hardware limitations, and diverse deployment scenarios. It emphasizes co-design of ML with physical systems and scalable, generalizable design flows for agile development.

ABSTRACT

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.

Motivation & Objective

• Address the unique challenges of deploying machine learning on tiny, low-cost, autonomous robots constrained by size, weight, power, and cost (SWAP).
• Identify the compounding difficulties arising from the intersection of embedded systems, robotics, and machine learning in tiny robot platforms.
• Propose holistic end-to-end system co-design as a solution to optimize performance under severe hardware and environmental constraints.
• Develop generalizable and automated design flows to navigate the vast diversity of robot deployment scenarios.
• Advance ML system design by integrating embedded TinyML techniques with robotics-specific requirements and physical system constraints.

Proposed method

• Conduct a survey of existing tiny robot platforms and their ML deployment challenges, focusing on SWAP and hardware limitations.
• Propose end-to-end benchmarking frameworks to quantify tradeoffs across the cyber-physical stack, including compute, sensors, actuators, and energy use.
• Integrate machine learning techniques such as model compression, distillation, and hybrid model-based/learning-based control to reduce computational and sensor demands.
• Leverage automated optimization techniques like Bayesian optimization, evolutionary algorithms, and reinforcement learning to explore the design space of neural network hyperparameters and hardware configurations.
• Design generalizable, open-source toolchains to encode domain-specific knowledge and enable automated, expert-free system generation across embedded systems, robotics, and ML.
• Co-design ML models with physical robot properties (e.g., battery weight, actuator precision) to maximize mission time and performance under constraints.

Experimental results

Research questions

• RQ1 How can machine learning be effectively deployed on tiny robots with severe size, weight, power, and cost constraints?
• RQ2 What are the key system-level tradeoffs in end-to-end design of tiny robot learning systems, and how can they be quantitatively evaluated?
• RQ3 To what extent can low-cost sensors (e.g., proximity, light) replace high-cost sensors (e.g., cameras, LIDAR) through ML-enhanced processing?
• RQ4 How can automated, generalizable design flows be constructed to efficiently navigate the vast design space of tiny robot learning systems?
• RQ5 What role can end-to-end co-design of ML with physical robot systems play in improving robustness and efficiency under resource constraints?

Key findings

• Tiny robot learning reveals the necessity of holistic ML system design due to the compounding constraints of embedded systems, robotics, and ML in a single platform.
• Low-cost sensors such as proximity and light sensors can be used effectively with ML to achieve perception levels comparable to cameras or LIDAR, reducing system weight and cost.
• Hybrid model-based and learning-based controllers enable tiny quadruped robots to walk over uneven terrain despite limited sensing and actuation capabilities.
• End-to-end benchmarking frameworks like Air Learning enable systematic exploration of tradeoffs between learning algorithms, models, sensing modalities, and compute platforms.
• Automated design flows using Bayesian optimization and reinforcement learning can efficiently navigate the complex design space of tiny robot learning systems.
• Generalizable, open-source toolchains for TinyML can be extended to support automated, full-stack design of tiny robot systems, reducing reliance on cross-domain expert intervention.