A novel framework for training a robotic fish to learn how to swim, even in the presence of degradations or failures in actuators is developed. Robotic underwater robots, particularly soft fish-inspired designs have gained significant attention due to their distinct benefits, including superior maneuverability, energy efficiency, versatile applications, and seamless integration with marine environments. However, their material properties and actuators can degrade, leading to pre-mature system failures. In this paper, we introduce the concept of actuator drop-out during training, to enable the robot to learn how to swim even when one or more actuators are degraded or non-functional. A Soft Actor-Critic Deep Reinforcement Learning architecture is used to learn a policy, with actuator degradations/failures introduced during training. A four actuator koi fish is modeled and simulated using the FishGym environment. Navigation-based validation tests show little degradation with one actuator failure, and much more robust swimming behaviors and performance compared to training with no failures, even when two or three actuators fail. These results will improve long-term operational reliability, ensuring robot fish functionality even in challenging underwater conditions.

The uncertainty quantification of prediction models (e.g., neural networks) is crucial for their adoption in many robotics applications. This is arguably as important as making accurate predictions, especially for safety-critical applications such as self-driving cars. This paper proposes our approach to uncertainty quantification in the context of visual localization for autonomous driving, where we predict locations from images. Our proposed framework estimates probabilistic uncertainty by creating a sensor error model that maps an internal output of the prediction model to the uncertainty. The sensor error model is created using multiple image databases of visual localization, each with ground-truth location. We demonstrate the accuracy of our uncertainty prediction framework using the Ithaca365 dataset, which includes variations in lighting, weather (sunny, snowy, night), and alignment errors between databases. We analyze both the predicted uncertainty and its incorporation into a Kalman-based localization filter. Our results show that prediction error variations increase with poor weather and lighting condition, leading to greater uncertainty and outliers, which can be predicted by our proposed uncertainty model. Additionally, our probabilistic error model enables the filter to remove ad hoc sensor gating, as the uncertainty automatically adjusts the model to the input data.

A self-driving car must be able to reliably handle adverse weather conditions (e.g., snowy) to operate safely. In this paper, we investigate the idea of turning sensor inputs (i.e., images) captured in an adverse condition into a benign one (i.e., sunny), upon which the downstream tasks (e.g., semantic segmentation) can attain high accuracy. Prior work primarily formulates this as an unpaired image-to-image translation problem due to the lack of paired images captured under the exact same camera poses and semantic layouts. While perfectly-aligned images are not available, one can easily obtain coarsely-paired images. For instance, many people drive the same routes daily in both good and adverse weather; thus, images captured at close-by GPS locations can form a pair. Though data from repeated traversals are unlikely to capture the same foreground objects, we posit that they provide rich contextual information to supervise the image translation model. To this end, we propose a novel training objective leveraging coarsely-aligned image pairs. We show that our coarsely-aligned training scheme leads to a better image translation quality and improved downstream tasks, such as semantic segmentation, monocular depth estimation, and visual localization.

Autonomous navigation is crucial for achieving the full automation of agricultural research and production management using agricultural robots. In this paper, we present a vision-based autonomous navigation approach for agriculture robots in trellised cropping systems, which stands out for its remarkable performance achieved entirely without human annotation. We propose a novel learning-based method that directly estimates the path traversibility heatmap from an RGB-D image and subsequently converts it into a preferred traversal path. One key advantage of our approach lies in its capability to predict the robot's preferred path directly, allowing us to obtain training labels without manual annotation. Specifically, we propose an automatic annotation pipeline that leverages the robot's path recorded during data collection. Furthermore, we develop a full navigation framework by integrating our path detection model with row switching modules, enabling the robot to smoothly transition between crop rows within the vineyard. We conduct extensive field trials in three different vineyards to validate the performance of our autonomous navigation framework. The results demonstrate that our approach provides a cost-effective, accurate, and robust solution for vineyard navigation.

Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and rain. In this paper, we present a new dataset to enable robust autonomous driving via a novel data collection process - data is repeatedly recorded along a 15 km route under diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time (day/night), and traffic conditions (pedestrians, cyclists and cars). The dataset includes images and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS to establish correspondence across routes. The dataset includes road and object annotations using amodal masks to capture partial occlusions and 3D bounding boxes. We demonstrate the uniqueness of this dataset by analyzing the performance of baselines in amodal segmentation of road and objects, depth estimation, and 3D object detection. The repeated routes opens new research directions in object discovery, continual learning, and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/

Shape and pose estimation is a critical perception problem for a self-driving car to fully understand its surrounding environment. One fundamental challenge in solving this problem is the incomplete sensor signal (e.g., LiDAR scans), especially for faraway or occluded objects. In this paper, we propose a novel algorithm to address this challenge, which explicitly leverages the sensor signal captured over consecutive time: the consecutive signals can provide more information about an object, including different viewpoints and its motion. By encoding the consecutive signals via a recurrent neural network, not only our algorithm improves the shape and pose estimates, but also produces a labeling tool that can benefit other tasks in autonomous driving research. Specifically, building upon our algorithm, we propose a novel pipeline to automatically annotate high-quality labels for amodal segmentation on images, which are hard and laborious to annotate manually. Our code and data will be made publicly available.

We present a probabilistic framework for detailed 3-D shape estimation and tracking using only vision measurements. Vision detections are processed via a bird's eye view representation, creating accurate detections at far ranges. A probabilistic model of the vision based point cloud measurements is learned and used in the framework. A 3-D shape model is developed by fusing a set of point cloud detections via a recursive Best Linear Unbiased Estimator (BLUE). The point cloud fusion accounts for noisy and inaccurate measurements, as well as minimizing growth of points in the 3-D shape. The use of a tracking algorithm and sensor pose enables 3-D shape estimation of dynamic objects from a moving car. Results are analyzed on experimental data, demonstrating the ability of our approach to produce more accurate and cleaner shape estimates.