27 accepted papers
Learning from Demonstration (LfD) is a well-studied field shown to provide robots with fundamental motion skills for a variety of domains. Significant research into various branches of LfD (e.g., learned dynamical systems and movement primitives) can generally be classified into those that learn ``t…
Enabling robots to manipulate articulated objects is essential for their successful integration into human-centric environments. Such manipulation is often part of a larger multistep task, where achieving a specific joint configuration is necessary for subsequent actions-for example, in a cluttered …
As robots' manipulation capabilities improve for pick-and-place tasks (e.g., object packing, sorting, and kitting), methods focused on understanding human-acceptable object configurations remain limited expressively with regard to capturing spatial relationships important to humans. To advance robot
There are a variety of mechanisms (i.e., input types) for real-time human interaction that can facilitate effective human-robot teaming. For example, previous works have shown how teleoperation, corrective, and discrete (i.e., preference over a small number of choices) input can enable robots to com
Object permanence, which refers to the concept that objects continue to exist even when they are no longer perceivable through the senses, is a crucial aspect of human cognitive development. In this work, we seek to incorporate this understanding into interactive robots by proposing a set of assumpt…
Explainable AI techniques that describe agent reward functions can enhance human-robot collaboration in a variety of settings. However, in order to effectively explain reward information to humans, it is important to understand the efficacy of different types of explanation techniques in scenarios o
Artificial neural nets can represent and classify many types of high-dimensional data but are often tailored to particular applications – e.g., for “fair” or “hierarchical” classification. Once an architecture has been selected, it is often difficult for humans to adjust models for a new task; for e…
Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models. In this work, we propose a set-based estimation algorithm, named Gaussian Process-Zonotopic Kalman Filter (GP-ZKF), that produces zonotopic state
Ensuring human safety without unnecessarily impacting task efficiency during human-robot interactive manipulation tasks is a critical challenge. In this work; we formally define human physical safety as collision avoidance or safe impact in the event of a collision. We developed a motion planner tha…
We present a task-and-motion planning (TAMP) algorithm robust against a human operator's cooperative or adversarial interventions. Interventions often invalidate the current plan and require replanning on the fly. Replanning can be computationally expensive and often interrupts seamless task executi…
As robots and other autonomous agents are increasingly incorporated into complex domains, characterizing interaction within heterogeneous teams that include both humans and machines becomes more necessary. Previous literature has addressed the task of characterizing human-robot interaction from diff…
In safety-critical environments, robots need to reliably recognize human activity to be effective and trust-worthy partners. Since most human activity recognition (HAR) approaches rely on unimodal sensor data (e.g. motion capture or wearable sensors), it is unclear how the relationship between the s…
The problem of temporal alignment of time series is common across many fields of study. Within the domain of robotics, human motion trajectories are one type of time series that is often utilized for recognition and prediction of human intent. In these applications, online temporal alignment of part…
Augmenting a robot with the capacity to understand the activities of the people it collaborates with in order to then label and segment those activities allows the robot to generate an efficient and safe plan for performing its own actions. In this work, we introduce an online activity segmentation…
In this work, we propose an algorithm that can plan safe and efficient robot trajectories in real time, given time-parameterized motion predictions, in order to avoid fast-moving obstacles in human-robot collaborative environments. Our algorithm is able to reduce the robot configuration space and th…
We consider human-robot collaboration in sequential tasks with known task objectives. For interaction planning in this setting, the utility of models for decision-making under uncertainty has been demonstrated across domains. However, in practice, specifying the model parameters remains challenging,
When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task. Prior research into learning from demonstrations (LfD) has failed to capture this notion of the acceptability of an…
Introducing mobile robots into the collaborative assembly process poses unique challenges for ensuring efficient and safe human-robot interaction. Current human-robot work cells require the robot to cease operating completely whenever a human enters a shared region of the given cell, and the robots
The ability to accurately predict human motion is imperative for any human-robot interaction application in which the human and robot interact in close proximity to one another. Although a variety of human motion prediction approaches have already been developed, they are often designed for specific…
Learning from demonstrations has been shown to be a successful method for non-experts to teach manipulation tasks to robots. These methods typically build generative models from demonstrations and then use regression to reproduce skills. However, this approach has limitations to capture hard geometr…
In this paper, we present Rapid Activity Prediction Through Object-oriented Regression (RAPTOR), a scalable method for performing rapid, real-time activity recognition and prediction that achieves state-of-the-art classification accuracy on both a generic human activity dataset and two domain-specif…
Solutions to robotic manipulation problems can be substantially improved through integrated task and motion planning. Existing approaches typically focus on satisfaction, finding a feasible solution, instead of optimization. We formulate large-scale robotic manipulation problems as multi-level optim…
Manipulation of deformable linear objects (DLO) has potential applications in aerospace and automotive assembly. The current literature on planning for deformable objects focuses on a single DLO at a time. In this paper, we provide a problem formulation for attaching a set of interlinked DLOs to a s
Interest in human-robot coexistence, in which humans and robots share a common work volume, is increasing in manufacturing environments. Efficient work coordination requires both awareness of the human pose and a plan of action for both human and robot agents in order to compute robot motion traject…
Mobile, interactive robots that operate in human-centric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence…
We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to directly report a global set of distinguishabl…