On-device Self-supervised Learning of Visual Perception Tasks on Nano-quadrotors

Core Concepts

The author proposes on-device learning for nano-drones to address domain shift issues in visual perception tasks, achieving significant performance improvements through self-supervised fine-tuning.

Abstract

This paper explores the challenges faced by sub-50g nano-drones with onboard deep learning models due to domain shift. The proposed solution involves self-supervised fine-tuning during in-field missions to enhance performance. By leveraging real-world vision-based regression tasks, the study delves into trade-offs related to dataset size, methodologies, and self-supervision strategies. The approach showcases up to a 30% improvement in mean absolute error compared to pre-trained models, requiring minimal fine-tuning time on ultra-low-power processors. The research aims to pave the way for advancements in robotics by addressing critical domain shift problems through on-device learning aboard nano-drones.

Stats

Our approach demonstrates an improvement in mean absolute error up to 30% compared to the pre-trained baseline. Requiring only 22 s fine-tuning on an ultra-low-power GWT GAP9 System-on-Chip. On the best-in-class GAP9 MCU, our approach requires just 22 s when fine-tuning only the batch-norm parameters.

Quotes

"We propose, for the first time, on-device learning aboard nano-drones." "Our promising results bring on-device learning aboard nano-drones as a viable way to address the critical domain shift problem."

Key Insights Distilled From

On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors

by Elia Cereda,... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04071.pdf

Deeper Inquiries

How can self-supervised learning impact other areas of robotics beyond visual perception tasks?

Self-supervised learning has the potential to revolutionize various aspects of robotics beyond just visual perception tasks. One key area where it can make a significant impact is in autonomous navigation. By allowing robots to learn from their own interactions with the environment, they can adapt and improve their navigation skills without relying on external supervision or labeled data. This could lead to more robust and adaptable robotic systems that can navigate complex environments with greater efficiency and accuracy. Another area where self-supervised learning can be beneficial is in manipulation tasks. Robots equipped with deep learning models trained through self-supervision can learn how to manipulate objects based on trial-and-error interactions, similar to how humans learn new tasks through practice. This approach could enable robots to perform delicate manipulation tasks in unstructured environments with minimal human intervention. Furthermore, self-supervised learning can enhance robot collaboration and coordination in multi-robot systems. By allowing individual robots to learn from their own experiences and share this knowledge with other robots in the network, they can collectively improve their performance as a team without requiring centralized control or extensive manual programming. In essence, self-supervised learning opens up possibilities for robots to become more autonomous, adaptive, and capable across a wide range of applications beyond visual perception tasks.

What are potential drawbacks or limitations of relying solely on onboard deep learning models for nano-drones?

While onboard deep learning models offer numerous advantages for nano-drones, there are also several drawbacks and limitations associated with relying solely on them: Limited Computational Resources: Nano-drones have constrained computational capabilities compared to larger drones or ground-based systems. Running complex deep learning algorithms onboard may strain these limited resources, leading to slower processing speeds or reduced model complexity. Power Consumption: Deep learning models require significant power consumption during inference and training processes. For nano-drones operating on battery power, this increased energy demand could limit flight time and overall mission duration. Domain Shift Challenges: Nano-drones deployed in real-world scenarios may encounter domain shift issues when the environmental conditions differ from those seen during training. Adapting onboard deep learning models quickly enough to address these shifts poses a challenge without access to fresh labeled data. Overfitting: Onboard deep learning models trained solely on specific datasets may suffer from overfitting when exposed to diverse real-world conditions not represented in the training data set. 5Safety Concerns: Reliance solely on onboard deep-learning algorithms raises safety concerns if the model fails unexpectedly due to unforeseen circumstances or adversarial attacks.

How might advancements in hardware technology further enhance the capabilities of nano-quadrotors beyond self-supervised Learning?

Advancements in hardware technology play a crucial role in enhancing the capabilities of nano-quadrotors beyond just leveraging self-supervised Learning: 1Improved Processing Power: More powerful processors integrated into nano-quadrotors would enable faster computation speeds for running complex algorithms such as reinforcement Learning or advanced computer vision techniques. 2Enhanced Sensor Integration: Advanced sensors like LIDAR (Light Detection And Ranging) coupled with improved processing units would provide better environmental awareness for navigation purposes. 3Energy-Efficient Designs: Energy-efficient hardware components would extend flight times by reducing power consumption while maintaining high computational performance. 4On-Chip Memory Expansion: Increased memory capacity directly impacts an AI system's ability by enabling larger datasets storage which leads towards better generalization abilities 5Real-Time Data Processing: Hardware improvements facilitating real-time data processing allow quadrotors' quick decision-making abilities based on changing environmental cues By incorporating these advancements into nano-quadrotor design, we pave the way for more sophisticated autonomous behaviors such as dynamic path planning obstacle avoidance even under challenging conditions - ultimately expanding their utility across various domains including surveillance agriculture disaster response etc..