153 citations · 716 across the 39 of their papers we have counts for
12 papers · 1 filter
Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations
Yanwei Wang, Nadia Figueroa, Shen Li +2
Learning from demonstration (LfD) has succeeded in tasks featuring a long time horizon. However, when the problem complexity also includes human-in-the-loop perturbations, state-of…
Explaining Reward Functions to Humans for Better Human-Robot Collaboration
Lindsay Sanneman, Julie Shah
Explainable AI techniques that describe agent reward functions can enhance human-robot collaboration in a variety of settings. One context where human understanding of agent reward…
Reactive Task and Motion Planning under Temporal Logic Specifications
Shen Li, Daehyung Park, Yoonchang Sung +2
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 a…
RoCUS: Robot Controller Understanding via Sampling
Yilun Zhou, Serena Booth, Nadia Figueroa +1
As robots are deployed in complex situations, engineers and end users must develop a holistic understanding of their behaviors, capabilities, and limitations. Some behaviors are di…
Experimental Assessment of Human-Robot Teaming for Multi-Step Remote Manipulation with Expert Operators
Claudia Pérez-D'Arpino, Rebecca P. Khurshid, Julie A. Shah
Remote robot manipulation with human control enables applications where safety and environmental constraints are adverse to humans (e.g. underwater, space robotics and disaster res…
The State of Industrial Robotics: Emerging Technologies, Challenges, and Key Research Directions
Lindsay Sanneman, Christopher Fourie, Julie A. Shah
Robotics and related technologies are central to the ongoing digitization and advancement of manufacturing. In recent years, a variety of strategic initiatives around the world inc…
Trust Considerations for Explainable Robots: A Human Factors Perspective
Lindsay Sanneman, Julie A. Shah
Recent advances in artificial intelligence (AI) and robotics have drawn attention to the need for AI systems and robots to be understandable to human users. The explainable AI (XAI…
Interactive Robot Training for Non-Markov Tasks
Ankit Shah, Samir Wadhwania, Julie Shah
Defining sound and complete specifications for robots using formal languages is challenging, while learning formal specifications directly from demonstrations can lead to over-cons…
Planning With Uncertain Specifications (PUnS)
Ankit Shah, Shen Li, Julie Shah
Reward engineering is crucial to high performance in reinforcement learning systems. Prior research into reward design has largely focused on Markovian functions representing the r…
Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators
Heitor J. Savino, Luciano C. A. Pimenta, Julie A. Shah +1
This paper presents a solution based on dual quaternion algebra to the general problem of pose (i.e., position and orientation) consensus for systems composed of multiple rigid-bod…
Mathematical Models of Adaptation in Human-Robot Collaboration
Stefanos Nikolaidis, Jodi Forlizzi, David Hsu +2
A robot operating in isolation needs to reason over the uncertainty in its model of the world and adapt its own actions to account for this uncertainty. Similarly, a robot interact…
Efficient Model Learning for Human-Robot Collaborative Tasks
Stefanos Nikolaidis, Keren Gu, Ramya Ramakrishnan +1
We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The…