One of the key issues in human-automated system interaction scenarios is deciding what decisions and tasks are best done with humans, or automated systems, or a combination of each. In general, these two entities possess complimentary skill sets. Humans perform extremely well dealing with unforeseen events or new tasks. On the other hand, autonomous systems excel in performing routine work tasks, and in well-defined problem areas. In the area of human-system interaction, our research focuses on using basic scientific and engineering methodologies necessary for improving human-agent system performance that results in a synergistic method for decision-making and task coordination in real-world scenarios.
Human-System Interaction
| Robot Learning | Assistive Robotics |
| Education and Robotics | Space Flight Life Support Systems |
As explorers, humans are superior to robots due to their ability to think critically, their resilience in the face of unexpected situations, and their adaptation to new scenarios. On the other hand, it is unrealistic to send humans in the near term on remote planetary missions or to hazardous terrain environments here on Earth. Although robots have limited perception and reasoning, and their capabilities are limited by foresight and insight of their own developers, it is more feasible to advance robot technology to function as explorers than to send humans. In the HumAnS Lab, we focus on increasing the capability of robot vehicles to function in natural environments, such as found on planetary surfaces, undersea, underground, and in remote geological locations here on Earth.
Space and Field Robotics
| SnoMotes | Arctic Navigation | Robot Surveying |
The focus of the lab is on control of autonomous systems that operate in the real-world. Thus, real-time capability is a necessary component of any technology used for control. To accomplish this goal, we develop techniques that allow real-time decision-making based on perceptions of the environment. Coupled with perception, autonomous reasoning allows a system to reason about actions based on its knowledge of the environment and information characterizing its current state within the environment. To incorporate reasoning for autonomous control, our focus is to embed human capabilities directly into the system, with a focus on developing a robotic control system having human-equivalent performance even after the interactions are complete.
Perception and Reasoning