The in-situ detection of planetary, lunar, and small-body surface terrain is crucial for autonomous spacecraft applications, where learning-based computer vision methods are increasingly employed to enable intelligence without prior information or human intervention. However, many of these methods remain computationally expensive for spacecraft processors and prevent real-time operation. Training of such algorithms is additionally complex due to the scarcity of labeled data and reliance on supervised learning approaches. Unsupervised Domain Adaptation (UDA) offers a promising solution by facilitating model training with disparate data sources such as simulations or synthetic scenes, although UDA is difficult to apply to celestial environments where challenging feature spaces are paramount. To alleviate such issues, You Only Crash Once (YOCOv1) has studied the integration of Visual Similarity-based Alignment (VSA) into lightweight one-stage object detection architectures to improve space terrain UDA. Although proven effective, the approach faces notable limitations, including performance degradations in multi-class and high-altitude scenarios. Building upon the foundation of YOCOv1, we propose novel additions to the VSA scheme that enhance terrain detection capabilities under UDA, and our approach is evaluated across both simulated and real-world data. Our second YOCO rendition, YOCOv2, is capable of achieving state-of-the-art UDA performance on surface terrain detection, where we showcase improvements upwards of 31% compared with YOCOv1 and terrestrial state-of-the-art. We demonstrate the practical utility of YOCOv2 with spacecraft flight hardware performance benchmarking and qualitative evaluation of NASA mission data.

Off-road navigation is essential for a wide range of applications in field robotics such as planetary exploration and disaster response. However, it remains an unresolved challenge due to the unstructured environments and inherent complexity of terrain-vehicle interactions. Traditional physics-based methods struggle to accurately model the nonlinear dynamics of these interactions, while data-driven approaches often suffer from overfitting to specific motion patterns, vehicle sizes, and types, limiting their generalizability. To overcome these challenges, we introduce a vision-based friction estimation framework grounded in neuro-symbolic principles, integrating neural networks for visual perception with symbolic reasoning for physical modeling. This enables significantly improved generalization abilities through explicit physical reasoning incorporating the predicted friction. Additionally, we develop a physics-informed planner that leverages the learned friction coefficient to generate physically feasible and efficient paths, along with corresponding speed profiles. We refer to our approach as AnyNav and evaluate it in both simulation and real-world experiments, demonstrating its utility and robustness across various off-road scenarios and multiple types of four-wheeled vehicles. These results mark an important step toward developing neuro-symbolic spatial intelligence to reason about complex, unstructured environments and enable autonomous off-road navigation in challenging scenarios. Video demonstrations are available at https://sairlab.org/anynav/, where the source code will also be released.

Developing excavation autonomy is challenging given the environments where excavators operate, the complexity of physical interaction and the degrees of freedom of operation of the excavator itself. Simulation is a useful tool to build parts of the autonomy without the complexity of experimentation. Traditional excavator simulators are geared towards high fidelity interactions between the joints or between the terrain but do not incorporate other challenges such as perception required for end to end autonomy. A complete simulator should be capable of supporting real time operation while providing high fidelity simulation of the excavator(s), the environment, and their interaction. In this paper we present TERA (Terrain Excavation Robot Autonomy), a simulator geared towards autonomous excavator applications based on Unity3D and AGX that provides the extensibility and scalability required to study full autonomy. It provides the ability to configure the excavator and the environment per the user requirements. We also demonstrate realistic dynamics by incorporating a time-varying model that introduces variations in the system's responses. The simulator is then evaluated with different scenarios such as track deformation, velocities on different terrains, similarity of the system with the real excavator and the overall path error to show the capabilities of the simulation.

The visual detection and tracking of surface terrain is required for spacecraft to safely land on or navigate within close proximity to celestial objects. Current approaches rely on template matching with pre-gathered patch-based features, which are expensive to obtain and a limiting factor in perceptual capability. While recent literature has focused on in-situ detection methods to enhance navigation and operational autonomy, robust description is still needed. In this work, we explore metric learning as the lightweight feature description mechanism and find that current solutions fail to address inter-class similarity and multi-view observational geometry. We attribute this to the view-unaware attention mechanism and introduce Multi-view Attention Regularizations (MARs) to constrain the channel and spatial attention across multiple feature views, regularizing the what and where of attention focus. We thoroughly analyze many modern metric learning losses with and without MARs and demonstrate improved terrain-feature recognition performance by upwards of 85%. We additionally introduce the Luna-1 dataset, consisting of Moon crater landmarks and reference navigation frames from NASA mission data to support future research in this difficult task. Luna-1 and source code are publicly available at https://droneslab.github.io/mars/.

Nature has evolved humans to walk on different terrains by developing a detailed understanding of their physical characteristics. Similarly, legged robots need to develop their capability to walk on complex terrains with a variety of task-dependent payloads to achieve their goals. However, conventional terrain adaptation methods are susceptible to failure with varying payloads. In this work, we introduce PANOS, a weakly supervised approach that integrates proprioception and exteroception from onboard sensing to achieve a stable gait while walking by a legged robot over various terrains. Our work also provides evidence of its adaptability over varying payloads. We evaluate our method on multiple terrains and payloads using a legged robot. PANOS improves the stability up to 44% without any payload and 53% with 15 lbs payload. We also notice a reduction in the vibration cost of 20% with the payload for various terrain types when compared to state-of-the-art methods.

Accurate feature detection is fundamental for various computer vision tasks, including autonomous robotics, 3D reconstruction, medical imaging, and remote sensing. Despite advancements in enhancing the robustness of visual features, no existing method measures the utility of visual information before processing by specific feature-type algorithms. To address this gap, we introduce PIXER and the concept of "Featureness," which reflects the inherent interest and reliability of visual information for robust recognition, independent of any specific feature type. Leveraging a generalization on Bayesian learning, our approach quantifies both the probability and uncertainty of a pixel's contribution to robust visual utility in a single-shot process, avoiding costly operations such as Monte Carlo sampling and permitting customizable featureness definitions adaptable to a wide range of applications. We evaluate PIXER on visual odometry with featureness selectivity, achieving an average of 31% improvement in RMSE trajectory with 49% fewer features.

Historically, feature-based approaches have been used extensively for camera-based robot perception tasks such as localization, mapping, tracking, and others. Several of these approaches also combine other sensors (inertial sensing, for example) to perform combined state estimation. Our work rethinks this approach; we present a representation learning mechanism that identifies visual features that best correspond to robot motion as estimated by an external signal. Specifically, we utilize the robot's transformations through an external signal (inertial sensing, for example) and give attention to image space that is most consistent with the external signal. We use a pairwise consistency metric as a representation to keep the visual features consistent through a sequence with the robot's relative pose transformations. This approach enables us to incorporate information from the robot's perspective instead of solely relying on the image attributes. We evaluate our approach on real-world datasets such as KITTI & EuRoC and compare the refined features with existing feature descriptors. We also evaluate our method using our real robot experiment. We notice an average of 49% reduction in the image search space without compromising the trajectory estimation accuracy. Our method reduces the execution time of visual odometry by 4.3% and also reduces reprojection errors. We demonstrate the need to select only the most important features and show the competitiveness using various feature detection baselines.

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code.

Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and fast travel in urban and suburban hubs. These UAM aircraft are conceived to operate from small airports called vertiports each comprising multiple take-off/landing and battery-recharging spots. Since they might be situated in dense urban areas and need to handle many aircraft landings and take-offs each hour, managing this schedule in real-time becomes challenging for a traditional air-traffic controller but instead calls for an automated solution. This paper provides a novel approach to this problem of Urban Air Mobility - Vertiport Schedule Management (UAM-VSM), which leverages graph reinforcement learning to generate decision-support policies. Here the designated physical spots within the vertiport's airspace and the vehicles being managed are represented as two separate graphs, with feature extraction performed through a graph convolutional network (GCN). Extracted features are passed onto perceptron layers to decide actions such as continue to hover or cruise, continue idling or take-off, or land on an allocated vertiport spot. Performance is measured based on delays, safety (no. of collisions) and battery consumption. Through realistic simulations in AirSim applied to scaled down multi-rotor vehicles, our results demonstrate the suitability of using graph reinforcement learning to solve the UAM-VSM problem and its superiority to basic reinforcement learning (with graph embeddings) or random choice baselines.

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization. PyPose's architecture is tidy and well-organized, it has an imperative style interface and is efficient and user-friendly, making it easy to integrate into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves more than $10\times$ speedup in computation compared to the state-of-the-art libraries. To boost future research, we provide concrete examples for several fields of robot learning, including SLAM, planning, control, and inertial navigation.