GNM: A General Navigation Model to Drive Any Robot (2210.03370v2)

Abstract: Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data. If we could combine data from all available sources, including multiple kinds of robots, we could train more powerful navigation models. In this paper, we study how a general goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots, and enable broad generalization across environments and embodiments. We analyze the necessary design decisions for effective data sharing across robots, including the use of temporal context and standardized action spaces, and demonstrate that an omnipolicy trained from heterogeneous datasets outperforms policies trained on any single dataset. We curate 60 hours of navigation trajectories from 6 distinct robots, and deploy the trained GNM on a range of new robots, including an underactuated quadrotor. We find that training on diverse data leads to robustness against degradation in sensing and actuation. Using a pre-trained navigation model with broad generalization capabilities can bootstrap applications on novel robots going forward, and we hope that the GNM represents a step in that direction. For more information on the datasets, code, and videos, please check out our project page https://sites.google.com/view/drive-any-robot.

• The paper introduces a universal navigation model that unifies control policies across various robot embodiments using a shared abstraction.
• The model leverages a normalized action space and context-based training on heterogeneous datasets to enhance adaptability across platforms.
• Experiments involving over 60 hours of trajectories on six distinct robots demonstrate robust zero-shot performance in unseen environments.

Overview of "GNM: A General Navigation Model to Drive Any Robot"

The paper "GNM: A General Navigation Model to Drive Any Robot" addresses the inherent challenges of training adaptable robot navigation models across diverse robotic platforms and environments. The research explores how training an omnipolicy on heterogeneous datasets from multiple robot embodiments can achieve broad generalization. This research contributes to the field of robotic vision-based navigation by aiming to develop a general navigation model (GNM) that can be leveraged to control an array of robots in various situations, without the requirement for robot-specific data collection.

Methodology

The authors designed a framework that leverages a shared abstraction to homogenize the action space across different robot types, thus simplifying the learning task. The approach includes defining a normalized action space for waypoints and training policies using temporal context to deduce robot-specific dynamics implicitly. The essence is in conditioning the policy on a context vector derived from a sequence of past observations, enabling the policy to adjust dynamically to different robotic platforms. The adoption of this unified action representation facilitates effective data sharing across robots, allowing the systems trained on vastly different embodiments to generalize more effectively.

Experimental Framework

The research encapsulates a comprehensive paper involving six different robotic platforms displaying a variety of dynamics and sensor configurations. A dataset of over 60 hours of navigation trajectories was curated to train the GNM, encompassing a mixture of indoor and outdoor environments. The multiplicity of robot types—from wheeled robots to an underactuated quadrotor—demonstrates the model's ability to perform zero-shot deployment on unexplored robots and environments.

Results and Implications

The GNM showcases a significant enhancement in navigating both seen and unseen environments when compared to single-domain policies. This cross-domain generalization validates the effectiveness of the shared action space and contextual conditioning. Moreover, the approach exhibits robustness to variations in environmental and robotic parameters, such as sensor misalignment or actuation degradation. This inherent robustness illustrates the promise of using GNM-style models to handle practical challenges that naturally occur in real-world robotic deployments.

Future Research Opportunities

While "GNM: A General Navigation Model to Drive Any Robot" presents a pivotal stride towards embodiment-agnostic navigation models, the scope of future research is substantial. A critical forward-looking avenue involves expanding the versatility of these models to accommodate a broader range of sensory inputs and more complex environments, such as those with varying spatial and operational constraints. Additional research could also explore the integration of multi-modal data and investigate domain adaptation techniques that enhance scalability to entirely new types of robots beyond ground-based visual navigation tasks.

Conclusion

The GNM represents a compelling advancement in the pursuit of universal, adaptable navigation models. By demonstrating that policies trained on diverse datasets can effectively control disparate robot types in challenging conditions, this work sets a precedent for the development of general-purpose navigation backbones. These backbones hold the potential to transform the implementation methodologies in robotic navigation tasks, paralleling the utility of pre-trained models in other AI domains. Through continued refinement and expansion, general navigation models could dramatically enhance the versatility and applicability of autonomous systems across an even broader spectrum of real-world environments and robotic platforms.