Vision-State Fusion: Enhancing Deep Neural Networks for Autonomous Robotics

Alapfogalmak

Leveraging the robot's state improves deep learning models' performance in non-egocentric perception tasks.

Kivonat

This paper explores the application of a novel methodology that integrates the robot's state into visual deep learning models to enhance spatial perception in robotics. The study includes three use cases: robot arm-to-object, drone-to-drone, and drone-to-human pose estimation. By incorporating the robot's state information, significant improvements in regression performance were observed across all use cases.

1. Introduction

• Vision-based deep learning plays a crucial role in robotics.
• Control-oriented end-to-end perception approaches commonly utilize the robot's state estimation as an auxiliary input.
• This work proposes integrating the robot's state into non-egocentric mediated tasks to improve regression performance.

2. Robot Arm-to-Object (A2O)

• MobileNetV2-based model shows substantial improvement with stateful integration.
• Stateful model outperforms baseline on x, y, z components of object pose estimation.

3. Drone-to-Drone (D2D)

• Adaptation of PULP-Frontnet for peer drone pose estimation.
• Stateful model demonstrates improved performance on all output variables compared to baseline.

4. Drone-to-Human (D2H)

• Comparison with PULP-Frontnet validates significant enhancement with stateful integration.
• In-field experiments show superior tracking and altitude control by the stateful model.

Statisztikák

Stateless model achieves R2 = -0.58 on z component in A2O use case.
Stateful model improves R2 scores up to +0.51 compared to baselines across all use cases.

Idézetek

"Our results consistently show benefits upon state-of-the-art model performance when leveraging the state input."

Főbb Kivonatok

Vision-State Fusion

by Elia Cereda,... : arxiv.org 03-21-2024

https://arxiv.org/pdf/2206.06112.pdf

Mélyebb kérdések

How does incorporating the robot's state impact real-time decision-making in autonomous systems?

Incorporating the robot's state into deep learning models can significantly impact real-time decision-making in autonomous systems. By providing the model with information about the current state of the robot, such as its position, orientation, velocity, and other relevant parameters, the model gains a better understanding of how to interpret sensory data and make decisions based on that information. This integration allows for more context-aware processing of visual inputs and enables the system to adapt its actions dynamically based on changes in its environment or internal conditions. For example, in a drone navigating through an obstacle course, knowing its own pitch angle can help adjust flight maneuvers to avoid collisions or maintain stability during windy conditions.

What are potential limitations or challenges associated with integrating state information into deep learning models for robotics?

While integrating state information into deep learning models for robotics offers numerous benefits, there are also some limitations and challenges to consider. One challenge is ensuring that the state information provided is accurate and up-to-date since any discrepancies could lead to incorrect decisions by the system. Additionally, incorporating additional inputs like robot states may increase computational complexity and memory requirements of the model, potentially impacting performance or requiring more resources for deployment.
Another limitation is related to generalization; if a model becomes too reliant on specific states or conditions during training, it may struggle when faced with new environments or scenarios where those states differ significantly. Balancing between leveraging state information effectively without overfitting to specific conditions is crucial for robust performance across various situations.
Furthermore, interpreting complex relationships between visual data and multiple dimensions of robot states can be challenging. Designing architectures that effectively fuse these inputs while maintaining interpretability and avoiding feature redundancy requires careful consideration.

How can these findings be applied to other fields beyond robotics to enhance machine learning algorithms?

The findings from incorporating robot states into deep learning models for robotics can be applied across various fields beyond just robotics:

Healthcare: In medical imaging analysis tasks like tumor detection or disease classification using MRI scans or X-rays, integrating patient-specific data (e.g., age, medical history) alongside image data could improve diagnostic accuracy.

Autonomous Vehicles: Similar techniques could enhance self-driving car systems by considering vehicle dynamics (speed, acceleration) along with sensor inputs (camera feeds) for safer navigation.

Finance: Incorporating market indicators along with historical trading data in stock price prediction models could lead to more informed investment decisions.

Environmental Monitoring: Combining environmental sensor readings with satellite imagery analysis could provide insights into climate change patterns or natural disaster forecasting.

By adapting these principles from robotic perception tasks to other domains,
machine learning algorithms stand poised
to benefit from richer contextual input,
leading
to improved decision-making capabilities
and enhanced overall performance across diverse applications.