18 accepted papers
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…
Behavior cloning has shown success in many sequential decision-making tasks by learning from expert demonstrations, yet they can be very sample inefficient and fail to generalize to unseen scenarios. One approach to these problems is to introduce general domain knowledge, such that the policy can fo
Large language models contain noisy general knowledge of the world, yet are hard to train or fine-tune. In contrast cognitive architectures have excellent interpretability and are flexible to update but require a lot of manual work to instantiate. In this work, we combine the best of both worlds: bo…
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…
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…
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…
A majority of the courses on autonomous systems focus on robotics, despite the growing use of autonomous agents in a wide spectrum of applications, from smart homes to intelligent traffic control. Our goal in designing a new senior-level undergraduate course is to teach the integration of a variety…
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…
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…
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.…
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 this paper, we propose a touch-based localization approach for a potentially large and complex object with multiple internal degrees of freedom. Should a task only require a partial localization of the object, our method selects the appropriate information gathering actions to register the desire…
Performing detailed work on objects requires precise localization. Currently humans aid machines in localization either by direct operation, or implicitly by designing a sequence of actions a robot follows. Our approach to automate localization is to reason over many potential actions, perform the b…
Expressive motion can situate a robot's attitude in its task motions, illustrating real-time reactions. Inspired by acting movement training, we construct path shape features that layer expression into a mobile robot's motion traversal. Our video-study results show that simple variations of path sha…
Assembly of large structures requires large fixtures, often referred to as monuments. Their cost and massive size limit flexibility and scalability of the manufacturing process. Numerous small mobile robots can replace these large structures and, therefore, replicate the efficiency of the assembly l…
Modeling the effects of actions based on the state of the world enables robots to make intelligent decisions in different situations. However, it is often infeasible to have globally accurate models. Task performance is often hindered by discrepancies between models and the real world, since the tru…