SALON: Self-supervised Adaptive Learning for Off-road Navigation (2412.07826v1)

Abstract: Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a tremendous amount of data and can be vulnerable to domain shifts. To improve generalization in novel environments, recent works have incorporated adaptation and self-supervision to develop autonomous systems that can learn from their own experiences online. However, current works often rely on significant prior data, for example minutes of human teleoperation data for each terrain type, which is difficult to scale with more environments and robots. To address these limitations, we propose SALON, a perception-action framework for fast adaptation of traversability estimates with minimal human input. SALON rapidly learns online from experience while avoiding out of distribution terrains to produce adaptive and risk-aware cost and speed maps. Within seconds of collected experience, our results demonstrate comparable navigation performance over kilometer-scale courses in diverse off-road terrain as methods trained on 100-1000x more data. We additionally show promising results on significantly different robots in different environments. Our code is available at https://theairlab.org/SALON.

• The paper introduces a novel self-supervised framework that generates adaptive costmaps and speedmaps to achieve high-performance off-road navigation.
• It employs a Birds-Eye-View mapping technique and one-shot cost augmentation to reduce reliance on extensive hand-labeled data.
• Real-world experiments across diverse robotic platforms confirm efficient and generalizable navigation with rapid adaptation from minimal experience.

SALON: Advancements in Self-Supervised Adaptive Learning for Off-Road Navigation

The paper SALON: Self-supervised Adaptive Learning for Off-road Navigation presents a novel methodology for autonomous robot navigation in challenging off-road environments. Traditional navigation methods face significant challenges in off-road terrains due to the lack of structured environments, resulting in a reliance on large amounts of hand-labeled data and a vulnerability to domain shifts. SALON introduces a self-supervised learning framework designed to address these challenges by enabling rapid adaptation to new environments with minimal human input.

Objectives and Framework

SALON aims to facilitate robotic navigation by employing a perception-action framework that allows for fast adaptation of traversability estimates. The method leverages self-supervised signals obtained from the robot's own experiences to update adaptive and risk-aware cost and speed maps. This framework enables the robot to produce high-performance navigation capabilities, comparable to models trained on substantially larger datasets, within a minimal time frame and without significant prior human involvement. The method is structured around several key innovations:

1. Mapping and Learning in Map Space: The visual features captured by the robot's camera are projected into a Birds-Eye-View (BEV) map, creating a more robust and clear map representation.
2. One-shot Cost Augmentation: Minimal human input is needed to initialize the system, which is achieved by user annotations on prior images to set initial avoidance markers.
3. Out-of-Distribution Detection: The system includes functionality to detect and respond to anomalies, thus reducing the need for teleoperation in various terrains.
4. Adaptive and Risk-Aware Mapping: By using the robot's experience, the method adjusts cost and speed maps to optimize navigation outcomes.

Contributions and Methodology

The authors make several contributions to the field of autonomous navigation:

• Development of the SALON framework, which generates adaptive costmaps and speedmaps using minimal human intervention.
• Real-world experimentation confirms SALON's ability to achieve navigation performance on par with state-of-the-art methods that rely on significantly larger datasets.
• Evidence of generalizability across different robotic platforms and environments by successful trials on diverse robots varying in dynamics, sensory configurations, and environments.

Experimental Results

The real-world experiments showcase SALON's effectiveness in various off-road terrains, maintaining navigation performance comparable to models trained with 100-1000 times more data, while requiring only seconds of collected experience. In qualitative comparisons, SALON performs well in distinguishing fine-grained terrain features and responding appropriately to dynamic terrain conditions.

The paper further demonstrates the practicality of SALON across various platforms, including a full-scale all-terrain vehicle, an autonomy-equipped wheelchair, and an ANYmal quadruped robot. This versatility underscores the robustness and adaptability of the framework, crucial for the deployment of autonomous systems in unstructured environments.

Implications and Future Work

The work suggests substantial theoretical and practical implications, providing insights into developing adaptive learning systems for varied conditions without extensive prior data collection or manual input. Future research might focus on addressing known limitations, such as exploring speedmap prediction under different system dynamics and establishing standardized benchmarks for off-road navigation.

SALON innovates by minimizing the human effort and data required for effective adaptation, enabling its application to a wide range of robotics platforms operating in diverse, unanticipated environments. This advancement marks a step forward in enhancing the autonomy and operational scope of robotic systems dealing with complex terrains.