ViNG: Learning Open-World Navigation with Visual Goals (2012.09812v2)

Abstract: We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform. Learning provides an appealing alternative to conventional methods for robotic navigation: instead of reasoning about environments in terms of geometry and maps, learning can enable a robot to learn about navigational affordances, understand what types of obstacles are traversable (e.g., tall grass) or not (e.g., walls), and generalize over patterns in the environment. However, unlike conventional planning algorithms, it is harder to change the goal for a learned policy during deployment. We propose a method for learning to navigate towards a goal image of the desired destination. By combining a learned policy with a topological graph constructed out of previously observed data, our system can determine how to reach this visually indicated goal even in the presence of variable appearance and lighting. Three key insights, waypoint proposal, graph pruning and negative mining, enable our method to learn to navigate in real-world environments using only offline data, a setting where prior methods struggle. We instantiate our method on a real outdoor ground robot and show that our system, which we call ViNG, outperforms previously-proposed methods for goal-conditioned reinforcement learning, including other methods that incorporate reinforcement learning and search. We also study how \sysName generalizes to unseen environments and evaluate its ability to adapt to such an environment with growing experience. Finally, we demonstrate ViNG on a number of real-world applications, such as last-mile delivery and warehouse inspection. We encourage the reader to visit the project website for videos of our experiments and demonstrations sites.google.com/view/ving-robot.

ViNG: Learning Open-World Navigation with Visual Goals

The paper "ViNG: Learning Open-World Navigation with Visual Goals" presents an innovative approach to robotic navigation by integrating learning-based methods with goal-conditioned reinforcement learning. The proposed system, termed ViNG, aims to enable mobile robots to navigate complex, unstructured environments using visual cues rather than relying solely on geometric maps or GPS data. This approach allows the robot to interpret visual goals without pre-existing knowledge of the environment's spatial layout, thus adapting more effectively to variable conditions such as lighting and appearance changes.

Key Insights

ViNG leverages three central innovations that distinguish it from traditional navigation algorithms:

1. Waypoint Proposal: This mechanism provides intermediate points that guide the robot towards the visual goal, facilitating navigation across larger distances and complex terrains.
2. Graph Pruning: By efficiently trimming the topological graph built from prior experiences, ViNG enhances the computational feasibility and traversal efficiency, maintaining relevant nodes that aid decision-making.
3. Negative Mining: During training, ViNG integrates negative examples to bolster the robustness of the traversability function. This inclusion counters potential distributional shifts by offering a wider spectrum of observations, thereby sharpening the model's scalability to diverse scenarios.

Performance and Generalization

The paper details empirical evaluations where ViNG outperforms existing goal-conditioned reinforcement learning approaches, showing superior efficacy in reaching distant goals. Moreover, ViNG demonstrates remarkable adaptability when transferred to novel environments with minimal additional training. This capability aligns with the premise that learning-based navigation systems can become self-improving, leveraging historical data to anticipate new navigational challenges without extensive retraining.

Practical Applications and Future Directions

The authors illustrate ViNG's practical utility in real-world settings such as autonomous delivery and inspection tasks, which are critical in GPS-denied environments or unmapped urban areas. Such applications highlight the potential of visual-based navigation systems to transform how robots interact with their surroundings autonomously.

Looking ahead, further research can focus on enhancing the system's resilience to dynamic changes, such as moving obstacles or shifting environmental elements. Integrating sensory fusion techniques or exploring hybrid models combining classical map-based planning with deep learning could extend ViNG's robustness. Additionally, pushing the boundaries in terms of faster adaptation, especially in rapidly changing environments, will be crucial for deploying ViNG in diverse, real-world conditions.

In summary, ViNG presents a significant contribution to the field of autonomous robotic navigation by bridging the gap between perception-driven and learning-driven methodologies. It underlines a direction where visual cues can become pivotal in guiding robots through intricate landscapes, seamlessly integrating learning mechanisms that enhance navigational decision-making and adaptability.