17 accepted papers
For effective human-robot collaboration, a robot must align its actions with human goals, even as they change mid-task. Prior approaches often assume fixed goals, reducing goal prediction to a one-time inference. However, in real-world scenarios, humans frequently shift goals, making it challenging
From dating to job interviews, making new friends or simply chatting with the cashier at checkout, engaging in small talk is a vital, everyday social skill. For adults with Autism Spectrum Disorder (ASD), small talk can be particularly challenging, yet it is essential for social integration, buildin…
The rapid and nearly pervasive impact of artificial intelligence on fields as diverse as medicine, law, banking, and the arts has made many students who would never enroll in a computer science class become interested in understanding elements of artificial intelligence. Fueled by questions about h…
In this work, we introduce and formalize the Zero-Knowledge Task Planning (ZKTP) problem, i.e., formulating a sequence of actions to achieve some goal without task-specific knowledge. Additionally, we present a first investigation and approach for ZKTP that leverages a large language model (LLM) to
In this work, we introduce a strategy that frames the sequential action selection problem for robots in terms of resolving blocking conditions, i.e., situations that impede progress on an action en route to a goal. This strategy allows a robot to make one-at-a-time decisions that take in pertinent c…
Robots are well-suited to alleviate the burden of repetitive and tedious manipulation tasks. In many applications though, a robot may be asked to interact with a wide variety of objects, making it hard or even impossible to pre-program visual object classifiers suitable for the task of interest. In…
Creating an accurate model of a user's skills is necessary for intelligent tutoring systems. Without an accurate model, sample problems or tasks must be selected haphazardly by the tutor. Once an accurate model has been trained, the tutor can selectively focus on training essential or deficient skil…
While abstract knowledge like cause-and-effect relations enables robots to problem-solve in new environments, acquiring such knowledge remains out of reach for many traditional machine learning techniques. In this work, we introduce a method for a robot to learn an explicit model of cause-and-effect…
Current robotic terrain mapping techniques require expensive sensor suites to construct an environmental representation. In this work, we present a cube-shaped robot that can roll through unstructured terrain and construct a detailed topographic map of the surface that it traverses in real time with…
Human-Robot Collaboration (HRC) aims to develop robots that provide assistance to human workers while performing physical tasks. Such assistance comes in the form of supportive behaviors that are different from the actions part of the task, and that are meant to help a human worker more effectively…
Research in human teamwork shows that a key element of fluid and fluent interactions is the interpretation of implicit verbal and non-verbal cues in context. This poses an issue to robotic platforms, however, as they have historically worked best when controlled through explicit commands that have e…
In this paper, we present a model set for designing human-robot collaboration (HRC) experiments. It targets a common scenario in HRC, which is the collaborative assembly of furniture, and it consists of a combination of standard components and custom designs. With this work, we aim at reducing the a…
Developing robots capable of making sense of their environment requires the ability to learn from observations. An important paradigm that allows for robots to both imitate humans and gain an understanding of the tasks people perform is that of action primitive discovery. Action primitives have been…
Collaborative robots represent a clear added value to manufacturing, as they promise to increase productivity and improve working conditions of such environments. Although modern robotic systems have become safe and reliable enough to operate close to human workers on a day-to-day basis, the workloa…
Collaboration between humans and robots requires solutions to an array of challenging problems, including multi-agent planning, state estimation, and goal inference. There already exist feasible solutions for many of these challenges, but they depend upon having rich task models. In this work we det
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
In this work, we present an algorithm for improving collaborator performance on sequential manipulation tasks. Our agent-decoupled, optimization-based, task and motion planning approach merges considerations derived from both symbolic and geometric planning domains. This results in the generation of…