16 accepted papers
Nonverbal communication through expressive robot motion significantly enhances human-robot interaction (HRI), yet designing motions that are both individually legible and collectively distinguishable remains challenging. Existing large language model (LLM)-based methods can successfully generate exp
This paper investigates the use of Video Foundation Models (ViFMs) for generating robot data summaries to enhance intermittent human supervision of robot teams. We propose a novel framework that produces both generic and query-driven summaries of long-duration robot vision data in three modalities:
Learning from Demonstration (LfD) is a powerful type of machine learning that can allow novices to teach robots to complete various tasks. However, the learning process for these systems may still be difficult for novices to interpret and understand, making effective teaching challenging. Explainabl
In shared human-robot environments, effective navigation requires robots to adapt to various pedestrian behaviors encountered in the real world. Most existing deep reinforcement learning algorithms for human-aware robot navigation typically assume that pedestrians adhere to a single walking behavior
Online evaluation is increasingly adopted in robotics research, providing an efficient approach to collect data from large and diverse populations. However, there have been ongoing debates about online studies as a proxy for in-person studies, especially where a participant passively observes video
Expressive robot motion is a form of nonverbal communication that enables robots to convey their internal states, fostering effective human-robot interaction. A key step in designing expressive robot motions is developing a mapping from the desired states the robot will express to the robot's hardwa
Robots make compulsory machine sounds, known as “consequential sounds,” as they move and operate. As robots become more prevalent in workplaces, homes and public spaces, understanding how sounds produced by robots affect human-perceptions of these robots is becoming increasingly important to creatin
Collaborative robots must effectively communicate their internal state to humans to enable a smooth interaction. Nonverbal communication is widely used to communicate information during human-robot interaction, however, such methods may also be misunderstood, leading to communication errors. In this
The ability to adapt to changing environments and settings is essential for robots acting in dynamic and unstructured environments or working alongside humans with varied abilities or preferences. This work introduces an extremely simple and effective approach to adapting neural models in response t…
We study gravitational pivoting, a constrained version of in-hand manipulation, where we aim to control the rotation of an object around the grip point of a parallel gripper. To achieve this, instead of controlling the gripper to avoid slip, we \emph{embrace slip} to allow the object to rotate in-ha…
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-expert end-users to teach robots. However, most LfD techniques assume users provide optimal demonstrations, which may not be accurate. Demonstration quality plays a crucial role in robot learning and generalization. Henc
Occlusions caused by a robot’s own body is a common problem for closed-loop control methods employed in eye-to-hand camera setups. We propose an optimization-based reactive controller that minimizes self-occlusions while achieving a desired goal pose. The approach allows coordinated control between
Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omnidirectional target with multiple, homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given
External feedback in the form of visual, auditory and tactile cues has been used to assist patients to overcome mobility challenges. However, these cues can become less effective over time. There is limited research on adapting cues to account for inter and intra-personal variations in cue responsiv
Unmanned aerial vehicles (UAVs) are often used for navigating dangerous terrains, however they are difficult to pilot. Due to complex input-output mapping schemes, limited perception, the complex system dynamics and the need to maintain a safe operation distance, novice pilots experience difficultie
Specifying complex task behaviors while ensuring good robot performance may be difficult for untrained users. We study a framework for users to specify rules for acceptable behavior in a shared environment such as industrial facilities. As non-expert users might have little intuition about how their