7 accepted papers
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 mane
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., seman…
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 approa…
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 rema…
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 unde…
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 prop…
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…