16 accepted papers
Robotics has long sought to develop robots capable of completing previously unseen long-horizon tasks. Hierarchical approaches offer a pathway for achieving this goal by executing skill combinations arranged by a task planner, with each visuomotor skill pre-trained using a specific imitation learnin
Vision-language-action (VLA) models present a promising paradigm by training policies directly on real robot datasets like Open X-Embodiment. However, the high cost of real-world data collection hinders further data scaling, thereby restricting the generalizability of VLAs. In this paper, we introdu
Robot Imitation Learning (IL) is a crucial technique in robot learning, where agents learn by mimicking human demonstrations. However, IL encounters scalability challenges stemming from both non-user-friendly demonstration collection methods and the extensive time required to amass a sufficient numb…
Large Language Models (LLMs) trained using massive text datasets have recently shown promise in generating action plans for robotic agents from high-level text queries. However, these models typically do not consider the robot's environment, resulting in generated plans that may not actually be exec…
Human-robot teams are expected to accomplish complex tasks in high-risk and uncertain environments. In domains such as space exploration or search & rescue, a human operator may not be a robotics expert, but will need to establish a baseline understanding of the robot's capabilities with respect to
When teleoperating complex robotic manipula-tors, operators often find it most natural to issue commands that dictate end effector movements in task space. If the robot has redundant degrees of freedom, the translation of this com-mand from task space into configuration space can affect the robot's…
Human-robot teams are expected to accomplish complex tasks in high-risk and uncertain environments. In domains such as space exploration or search & rescue, a human operator may not be a robotics expert, but will need to establish a baseline understanding of the robot's capabilities with respect to…
Collaborative human-robot field operations rely on timely decision-making and coordination, which can be challenging for heterogeneous teams operating in large-scale deployments. In this work, we present the design of an immersive, mixed reality (MR) interface to support sense-making and situational…
Learning from Demonstration (LfD) enables novice users to teach robots new skills. However, many LfD methods do not facilitate skill maintenance and adaptation. Changes in task requirements or in the environment often reveal the lack of resiliency and adaptability in the skill model. To overcome the…
Robotic technologies are becoming pervasive within industrial and domestic settings, resulting in more frequent interactions between humans and robots. To ensure these interactions are effective, Human-Robot Interaction (HRI) researchers have argued that robots and humans must establish a shared com…
We present PufferBot, an aerial robot with an expandable structure that may expand to protect a drone's propellers when the robot is close to obstacles or collocated humans. PufferBot is made of a custom 3D-printed expandable scissor structure, which utilizes a one degree of freedom actuator with ra…
In shared control, advances in autonomous robotics are applied to help empower a human user in operating a robotic system. While these systems have been shown to improve efficiency and operation success, users are not always accepting of the new control paradigm produced by working with an assistive…
This letter assesses an adaptive approach to fault recovery in autonomous robotic space operations, which uses indicators of opportunity, such as physiological state measurements and observations of past human assistant performance, to inform future selections. We validated our reinforcement learnin
This letter considers the problem of how robots in long-term space operations can learn to choose appropriate sources of assistance to recover from failures. Current assistant selection methods for failure handling are based on manually specified static lookup tables or policies, which are not respo
Successful human-robot collaboration depends on a shared understanding of task state and current goals. In nonlinear or freeform tasks without an explicit task model, robot partners are unable to provide assistance without the ability to translate perception into meaningful task knowledge. In this p…
Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address…