13 accepted papers
Robots are essential for tasks that are hazardous or beyond human capabilities. However, the results of the Defense Advanced Research Projects Agency (DARPA) Subterranean (SubT) Challenge revealed that despite various techniques for robot autonomy, human input is still required in some complex situa
Curriculum learning has proven highly effective to speed up training convergence with improved performance in a variety of tasks. Researchers have been studying how a curriculum can be constituted to train reinforcement learning (RL) agents in various application domains. However, discovering curric
Human-robot handover is a key capability of service robots, such as those used to perform routine logistical tasks for healthcare workers. Recent algorithms have achieved tremendous advances in object-agnostic end-to-end planar grasping with up to six degrees of freedom (DoF); however, compiling the
Deep reinforcement learning has achieved signif-icant results in low-level controlling tasks. However, for some applications like autonomous driving and drone flying, it is difficult to control behavior stably since the agent may suddenly change its actions which often lowers the controlling sys-tem…
Marine debris is a problem both for the health of marine environments and for the human health since tiny pieces of plastic called “microplastics” resulting from the debris decomposition over the time are entering the food chain at any levels. For marine debris detection and removal, autonomous unde
This paper presents an easy-to-deploy, virtual reality-based teleoperation system for controlling a robot arm. The proposed system is based on a consumer-grade virtual reality device (Oculus Quest 2) with a low-cost robot arm (a LoCoBot) to allow easy replication and set up. The proposed Work-from-H…
Deep reinforcement learning (RL), where the agent learns from mistakes, has been successfully applied to a variety of tasks. With the aim of learning collision-free policies for unmanned vehicles, deep RL has been used for training with various types of data, such as colored images, depth images, an
Duckiepond is an education and research development environment that includes software systems, educational materials, and of a fleet of autonomous surface vehicles Duckieboat. Duckieboats are designed to be easily reproducible with parts from a 3D printer and other commercially available parts, wit…
The Amazon Picking Challenge and the Amazon Robotics Challenge have shown significant progress in object picking from a cluttered scene, yet object placement remains challenging. It is useful to have pose-aware placement based on human and machine readable pieces on an object. For example, the brand…
Navigation in pedestrian environments is critical to enabling independent mobility for the blind and visually impaired (BVI) in their daily lives. White canes have been commonly used to obtain contact feedback for following walls, curbs, or man-made trails, whereas guide dogs can assist in avoiding…
Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies…
This work introduces a wearable system to provide situational awareness for blind and visually impaired people. The system includes a camera, an embedded computer and a haptic device to provide feedback when an obstacle is detected. The system uses techniques from computer vision and motion planning…
Navigating in a previously unknown environment and recognizing naturally occurring text in a scene are two important autonomous capabilities that are typically treated as distinct. However, these two tasks are potentially complementary, (i) scene and pose priors can benefit text spotting, and (ii) t…