Research Posts

NSF NRI-Small: Understanding Neuromuscular Adaptations in Human-Robot Physical Interaction for Adaptive Robot Co-Workers

ForceFeedback Mechanism

The goal of this award is to develop theories, methods, and tools to understand the mechanisms of neuromotor adaptation in human-robot physical interaction.  Human power-assisting systems, e.g., powered lifting devices that aid human operators in manipulating heavy or bulky loads, require physical contact between the operator and machine, creating a coupled dynamic system. This coupled dynamic has been shown to introduce inherent instabilities and performance degradation due to a change in human stiffness; when instability is encountered, a human operator often attempts to control the oscillation by stiffening their arm, which leads to a stiffer system with more instability.  The project will establish control algorithms for robot co-workers that proactively adjust the contact impedance between the operator and robotic manipulator for achieving higher performance and stability. This research will 1) understand the association between neuromuscular adaptations and system performance limits, 2) develop probabilistic methods to classify and predict the transition of operator’s cognitive and physical states from physiological measures, and 3) integrate this knowledge into a structure of shared human-robot and demonstrate the efficacy in a powered lifting device with real-world constraints at vehicle assembly facilities.

If successful, the research will benefit the communities interested in the adaptive shared control approach for advanced manufacturing and process design, including automobile, aerospace, and military. Such next-generation manufacturing is expected to improve productivity and reduce assembly time as well as the physical burden of assembly line workers. Research outcomes will be integrated into current courses at both graduate and undergraduate levels.

This work is in collaboration with Dr. Jun Ueda (PI), Dr. Minoru Shinohara, and Dr. Wayne Book


Divya Srivastava Presents at 5th International Conference on Human Computer Interaction Theory and Applications (HUCAPP 2021)

FEB 10, 2021 — 3rd-year CEC graduate student, Divya Srivastava, presents her work, “Effect of Interaction Design of Reinforcement Learning Agents on Human Satisfaction in Partially Observable Domains” virtually at the 5th International Conference on Human Computer Interaction Theory and Applications (HUCAPP 2021). The work is coauthored by Spencer Frazier (GT’s Human-Centered AI Lab), Dr. Mark Reidl (GT’s Human-Centered AI Lab), and Dr. Karen Feigh.

NSTRF – Decision Support System Development for Human Extravehicular Activity


Human spaceflight is arguably one of mankind’s most challenging engineering feats, requiring carefully crafted synergy between human and technological capabilities. One critical component of human spaceflight pertains to the activity conducted outside the safe confines of the spacecraft, known as Extravehicular Activity (EVA). Successful execution of EVAs requires significant effort and real-time communication between astronauts who perform the EVA and the ground personnel who provide real-time support. As NASA extends human presence into deep space, the time delay associated with communication relays between the flight crew and support crew will cause a shift from a real-time to an asynchronous communication environment. Asynchronous communication has been identified in the literature as an operational issue that must be addressed to ensure future mission success. There is a need to infuse advanced technologies with onboard systems to support crew decision-making in the absence of ground support. A decision support system (DSS) is one possible solution to enhance astronauts’ capability to identify, diagnose, and recover from time critical irregularities during EVAs without relying on real-time ground support.

The intent of this work is to (1) identify the system constraints on EVA operations, (2) develop the requirements for a DSS for operation within an asynchronous communication environment, (3) identify the characteristics of the DSS design are likely to fulfill the DSS requirements, and (4) assess how well the prototyped DSS performs in asynchronous EVA. The proposed research aims to examine how the EVA work domain is currently established using a constraint-based cognitive engineering framework to inform the design of a DSS. The prototype will then undergo an iterative design and evaluation process within a simulated asynchronous EVA environment. This thesis will contribute the underlying science needed to design a DSS within the EVA work domain to enable future mission operations. 

ONR – Interactive Machine Learning


We are interested in machines that can learn new things from people who are not Machine Learning (ML) experts. We propose a research agenda framed around the human factors (HF) and ML research questions of teaching an agent via demonstration and critique. Ultimately, we will develop a training simulation game with several nonplayer characters, all of which can be easily taught new behaviors by an end-user.

With respect to the Science of Autonomy, this proposal is focused on Interactive Intelligence. We seek to understand how an automated system can partner with a human to learn how to act and reason about a new domain. Interactive learning machines that adapt to the needs of a user have long been a goal of AI research. Machine Learning (ML) promises a way to build adaptive systems while avoiding tedious pre-programming (which in sufficiently complex domains is almost impossible); however, we have yet to see many successful applications where machines learn from everyday users. ML techniques are not designed for input from na¨ıve users, remaining by and large a tool built by experts for experts.
Many prior efforts in designing Machine Learning systems with human input, pose the problem as: “what can I get the human to do to help my machine learn better?” Instead, our goal is for systems to learn from everyday people, we reframe the problem as: “how can machines take better advantage of the input that an everyday person is going to be able to provide?” This approach is Interactive Machine Learning (IML), and brings Human Factors to the problem of Machine Learning. IML has two major complementary research goals: (1) to develop interaction protocols for people to teach an ML agent in a way they find natural and intuitive. (2) to design ML algorithms that take better advantage of a human teacher’s guidance; that is, to understand formally how to optimize the information source that is humans, even when those humans have imperfect models of the learning algorithms or suboptimal policies themselves. Our research agenda addresses both of these IML research questions in two complementary types of learning interactions:

  • Learning from Demonstrations (LfD)—A human teacher provides demonstrations of the desired behavior in a given task domain, from which the agent infers a policy of action.
  • Learning from Critique—A human teacher watches critiques the behavior with high-level feedback.

This project is the joint effort with Dr. Andrea Thomaz (PI), Dr. Charles Isbell and Dr. Mark Riedl.

ONR STTR – Designing Contextual Decision Support Systems


Support improved decision making under high stress, uncertain operational conditions through the development of proactive, context-based decision support aids. The objective of this project is to create a scientifically-principled design specification and prototype concepts for a set of decision aids capable of supporting decision making and judgment across multi-faceted mission with dynamic tasking requirements. The result will be a consistent approach to proactive decision support that will facilitate rapid, affordable development for different functions in the combat center, minimize training, insertion into combat systems, and increase end user adoption and utilization.

ONR – Overall Decision Making Process Simulation


Decision makers are consistently asked to make decisions about the course of action required to achieve mission success regardless of the time pressure and the quantity and quality of information available. To be successful, they will adapt their decision strategies to the environment and even use heuristics, simple rules that use little information and can be processed quickly. To support these decision makers, we are designing proactive decision support systems that support adaptive decision making along a range analytic and heuristic strategies.

NASA Authority & Autonomy 2010-2013


NextGen systems are envisioned to be composed of human and automated agents interacting with dynamic flexibility in the allocation of authority and autonomy. The analysis of such concepts of operation requires methods for verifying and validating that the range of roles and responsibilities potentially assignable to the human and automated agents does not lead to unsafe situations. Such analyses must consider the conditions that could impact system safety including the environment, human behavior and operational procedures, methods of collaboration and organization structures, policies and regulations. 

              Agent-based simulation has shown promise toward modeling such complexity but requires a tradeoff between fidelity and the number of simulation runs that can be explored in a reasonable amount of time. Model checking techniques can verify that the modeled system meets safety properties but they require that the component models are of sufficiently limited scope so as to run to completion. By analyzing simulation traces, model checking can also help to ensure that the simulation’s design meets the intended analysis goals. Thus leveraging these types of analysis methods can help to verify operational concepts addressing the allocation of authority and autonomy. To make the analyses using both techniques more efficient, common representations for model components, methods for identifying the appropriate safety properties, and techniques for determining the set of analyses to run are required.

This project is performed in association with Dr. Ellen Bass of Drexel University, Dr. Elsa Gunter of the University of Illinios, and John Rushby of SRI.