20 accepted papers
End-to-end visuomotor policies trained using behavior cloning have shown a remarkable ability to generate complex, multi-modal low-level robot behaviors. However, at deployment time, these policies still struggle to act reliably when faced with out-of-distribution (OOD) visuals induced by objects, b…
In interactive imitation learning (IL), uncertainty quantification offers a way for the learner (i.e. robot) to contend with distribution shifts encountered during deployment by actively seeking additional feedback from an expert (i.e. human) online. Prior works use mechanisms like ensemble disagree…
Assistive robotic arms often have more degrees-of-freedom than a human teleoperator can control with a low-dimensional input, like a joystick. To overcome this challenge, existing approaches use data-driven methods to learn a mapping from low-dimensional human inputs to high-dimensional robot action…
Intelligent driving assistance can alert drivers to objects in their environment; however, such systems require a model of drivers' situational awareness (SA) (what aspects of the scene they are already aware of) to avoid unnecessary alerts. Moreover, collecting the data to train such an SA model i…
As robots are deployed in human spaces, it is important that they are able to coordinate their actions with the people around them. Part of such coordination involves ensuring that people have a good understanding of how a robot will act in the environment. This can be achieved through explanations…
The ability to identify important objects in a complex and dynamic driving environment is essential for autonomous driving agents to make safe and efficient driving decisions. It also helps assistive driving systems decide when to alert drivers. We tackle object importance estimation in a data-drive
In this work, we aim to improve transparency and efficacy in human-robot collaboration by developing machine teaching algorithms suitable for groups with varied learning capabilities. While previous approaches focused on tailored approaches for teaching individuals, our method teaches teams with var…
In human-robot collaboration, both human and robotic agents must work together to achieve a set of shared objectives. However, each team member may have individual preferences, or constraints, for how they would like to contribute to the task. Effective teams align their actions to optimize task per…
Human and robot partners increasingly need to work together to perform tasks as a team. Robots designed for such collaboration must reason about how their task-completion strategies interplay with the behavior and skills of their human team members as they coordinate on achieving joint goals. Our go…
Shared control systems can make complex robot teleoperation tasks easier for users. These systems predict the user's goal, determine the motion required for the robot to reach that goal, and combine that motion with the user's input. Goal prediction is generally based on the user's control input (e.…
Research on Interactive Robot Learning has yielded several modalities for querying a human for training data, including demonstrations, preferences, and corrections. While prior work in this space has focused on optimizing the robot's queries within each interaction type, there has been little work…
To collaborate well with robots, we must be able to understand their decision making. Humans naturally infer other agents' beliefs and desires by reasoning about their observable behavior in a way that resembles inverse reinforcement learning (IRL). Thus, robots can convey their beliefs and desires…
Assistive robot manipulators help people with upper motor impairments perform tasks by themselves. However, teleoperating a robot to perform complex tasks is difficult. Shared control algorithms make this easier: these algorithms predict the user’s goal, autonomously generate a plan to accomplish th…
The ability to learn from large quantities of complex data has led to the development of intelligent agents such as self-driving cars and assistive devices. This data often comes from people via interactions such as labeling, providing rewards and punishments, and giving demonstrations or critiques.…
In shared autonomy, the user input is blended with the assistive motion to accomplish a task where the user goal is typically unknown to the robot. Transparency between the human and robot is essential for effective collaboration. Prior works have provided methods for the robot to infer the user goa…
Human-in-the-loop Machine Learning (HIL-ML) is a widely adopted paradigm for instilling human knowledge in autonomous agents. Many design choices influence the efficiency and effectiveness of such interactive learning processes, particularly the interaction type through which the human teacher may p…
In robot telemanipulation tasks, the robot can sometimes occlude a target object from the user's view. We investigate the potential of diminished reality to address this problem. Our method uses an optical see-through head-mounted display to create a diminished reality illusion that the robot is tra…
Robots operating in human environments must be careful, when executing their manipulation skills, not to disturb nearby objects. This requires robots to reason about the effect of their manipulation choices by accounting for the support relationships among objects in the scene. Humans do this in par…
Our human-robot collaboration research aims to improve the fluency and efficiency of interactions between humans and robots when executing a set of tasks in a shared workspace. During human-robot collaboration, a robot and a user must often complete a disjoint set of tasks that use an overlapping se…
This paper presents a model that uses a robot's verbal and nonverbal behaviors to successfully communicate object references to a human partner. This model, which is informed by computer vision, human-robot interaction, and cognitive psychology, simulates how low-level and high-level features of the