Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link

https://hdl.handle.net/1853/70657

Includes Organization(s)

Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 11

Towards a terradynamics of legged locomotion on homogeneous and Heterogeneous granular media through robophysical approaches

(Georgia Institute of Technology, 2015-11-16) Qian, Feifei

The objective of this research is to discover principles of ambulatory locomotion on homogeneous and heterogeneous granular substrates and create models of animal and robot interaction within such environments. Since interaction with natural substrates is too complicated to model, we take a robophysics approach – we create a terrain generation system where properties of heterogeneous multi-component substrates can be systematically varied to emulate a wide range of natural terrain properties such as compaction, orientation, obstacle shape/size/distribution, and obstacle mobility within the substrate. A schematic of the proposed system is discussed in detail in the body of this dissertation. Control of such substrates will allow for the systematic exploration of parameters of substrate properties, particularly substrate stiffness and heterogeneities. With this terrain creation system, we systematically explore locomotor strategies of simplified laboratory robots when traversing over different terrain properties. A key feature of this proposed work is the ability to generate general interaction models of locomotor appendages with such complex substrates. These models will aid in the design and control of future robots with morphologies and control strategies that allow for effective navigation on a large diversity of terrains, expanding the scope of terramechanics from large tracked and treaded vehicles on homogeneous ground to arbitrarily shaped and actuated locomotors moving on complex heterogeneous terrestrial substrates.
Developing an engagement and social interaction model for a robotic educational agent

(Georgia Institute of Technology, 2015-11-16) Brown, LaVonda N.

Effective educational agents should accomplish four essential goals during a student's learning process: 1) monitor engagement, 2) re-engage when appropriate, 3) teach novel tasks, and 4) improve retention. In this dissertation, we focus on all of these objectives through use of a teaching device (computer, tablet, or virtual reality game) and a robotic educational agent. We begin by developing and validating an engagement model based on the interactions between the student and the teaching device. This model uses time, performance, and/or eye gaze to determine the student's level of engagement. We then create a framework for implementing verbal and nonverbal, or gestural, behaviors on a humanoid robot and evaluate its perception and effectiveness for social interaction. These verbal and nonverbal behaviors are applied throughout the learning scenario to re-engage the students when the engagement model deems it necessary. Finally, we describe and validate the entire educational system that uses the engagement model to activate the behavioral strategies embedded on the robot when learning a new task. We then follow-up this study to evaluate student retention when using this system. The outcome of this research is the development of an educational system that effectively monitors student engagement, applies behavioral strategies, teaches novel tasks, and improves student retention to achieve individualized learning.
Developing robots that impact human-robot trust in emergency evacuations

(Georgia Institute of Technology, 2015-11-10) Robinette, Paul

High-risk, time-critical situations require trust for humans to interact with other agents even if they have never interacted with the agents before. In the near future, robots will perform tasks to help people in such situations, thus robots must understand why a person makes a trust decision in order to effectively aid the person. High casualty rates in several emergency evacuations motivate our use of this scenario as an example of a high-risk, time-critical situation. Emergency guidance robots can be stored inside of buildings then activated to search for victims and guide evacuees to safety. In this dissertation, we determined the conditions under which evacuees would be likely to trust a robot in an emergency evacuation. We began by examining reports of real-world evacuations and considering how guidance robots can best help. We performed two simulations of evacuations and learned that robots could be helpful as long as at least 30% of evacuees trusted their guidance instructions. We then developed several methods for a robot to communicate directional information to evacuees. After performing three rounds of evaluation using virtually, remotely and physically present robots, we concluded that robots should communicate directional information by gesturing with two arms. Next, we studied the effect of situational risk and the robot's previous performance on a participant's decision to use the robot during an interaction. We found that higher risk scenarios caused participants to align their self-reported trust with their decisions in a trust situation. We also discovered that trust in a robot drops after a single error when interaction occurs in a virtual environment. After an exploratory study in trust repair, we have learned that a robot can repair broken trust during the emergency by apologizing for its prior mistake or giving additional information relevant to the situation. Apologizing immediately after the error had no effect. Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we created a virtual environment of an office as well as a real-world simulation of an emergency evacuation. In both, participants interacted with a robot during a non-emergency phase to experience its behavior and then chose whether to follow the robot’s instructions during an emergency phase or not. In the virtual environment, the emergency was communicated through text, but in the real-world simulation, artificial smoke and fire alarms were used to increase the urgency of the situation. In our virtual environment, we confirmed our previous results that prior robot behavior affected whether participants would trust the robot or not. To our surprise, all participants followed the robot in the real-world simulation of an emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to exit the way they entered. The conclusions of this dissertation are based on the results of fifteen experiments with a total of 2,168 participants (2,071 participants in virtual or remote studies conducted over the internet and 97 participants in physical studies on campus). We have found that most human evacuees will trust an emergency guidance robot that uses understandable information conveyance modalities and exhibits efficient guidance behavior in an evacuation scenario. In interactions with a virtual robot, this trust can be lost because of a single error made by the robot, but a similar effect was not found with real-world robots. This dissertation presents data indicating that victims in emergency situations may overtrust a robot, even when they have recently witnessed the robot malfunction. This work thus demonstrates concerns which are important to both the HRI and rescue robot communities.
Using multiple agents in uncertainty minimization of ablating target sources

(Georgia Institute of Technology, 2014-09-05) Coogle, Richard A.

The objective of this research effort is to provide an efficient methodology for a multi-agent robotic system to observe moving targets that are generated from an ablation process. An ablation process is a process where a larger mass is reduced in volume as a result of erosion; this erosion results in smaller, independent masses. An example of such a process is the natural process that gives rise to icebergs, which are generated through an ablation process referred to as ice calving. Ships that operate in polar regions continue to face the threat of floating ice sheets and icebergs generated from the ice ablation process. Although systems have been implemented to track these threats with varying degrees of success, many of these techniques require that the operations are conducted outside of some boundary where the icebergs are known not to drift. Since instances where polar operations must be conducted within such a boundary line do exist (e.g., resource exploration), methods for situational awareness of icebergs for these operations are necessary. In this research, efficacy of these methods is correlated to the initial acquisition time of observing newly ablated targets, as it provides for the ability to enact early countermeasures. To address the research objective, the iceberg tracking problem is defined such that it is re-cast within a class of robotic, multiagent target-observation problems. From this new definition, the primary contributions of this research are obtained: 1) A definition of the iceberg observation problem that extends an existing robotic observation problem to the requirements for the observation of floating ice masses; 2) A method for modeling the activity regions on an ablating source to extract ideal search regions to quickly acquire newly ablated targets; 3) A method for extracting metrics for this model that can be used to assess performance of observation algorithms and perform resource allocation. A robot controller is developed that implements the algorithms that result from these contributions and comparisons are made to existing target acquisition techniques.
Robot learners: interactive instance-based learning with social robots

(Georgia Institute of Technology, 2014-04-11) Park, Hae Won

On one hand, academic and industrial researchers have been developing and deploying robots that are used as educational tutors, mediators, and motivational tools. On the other hand, an increasing amount of interest has been placed on non-expert users being able to program robots intuitively, which has led to promising research efforts in the fields of machine learning and human-robot interaction. This dissertation focuses on bridging the gap between the two subfields of robotics to provide personalized experience for the users during educational, entertainment, and therapeutic sessions with social robots. In order to make the interaction continuously engaging, the workspace shared between the user and the robot should provide personalized contexts for interaction while the robot learns to participate in new tasks that arise. This dissertation aims to solve the task-learning problem using an instance-based framework that stores human demonstrations as task instances. These instances are retrieved when confronted with a similar task in which the system generates predictions of task behaviors based on prior solutions. The main issues associated with the instance-based approach, i.e., knowledge encoding and acquisition, are addressed in this dissertation research using interactive methods of machine learning. This approach, further referred to as interactive instance-based learning (IIBL), utilizes the keywords people use to convey task knowledge to others to formulate task instances. The key features suggested by the human teacher are extracted during the demonstrations of the task. Regression approaches have been developed in this dissertation to model similarities between cases for instance retrieval including multivariate linear regression and sensitivity analysis using neural networks. The learning performance of the IIBL methods were then evaluated while participants engaged in various block stacking and inserting scenarios and tasks on a touchscreen tablet with a humanoid robot Darwin. In regard to end-users programming robots, the main benefit of the IIBL framework is that the approach fully utilizes the explanatory behavior of the instance-based method which makes the learning process transparent to the human teacher. Such an environment not only encourages the user to produce better demonstrations, but also prompts the user to intervene at the moment a new instance is needed. It was shown through user studies that participants naturally adapt their teaching behavior to the robot learner's progress and adjust the timing and the number of demonstrations. It was also observed that the human-robot teaching and learning scenarios facilitate the emergence of various social behaviors from participants. Encouraging social interaction is often an objective of the task especially with children with cognitive disabilities, and a pilot study with children with autism spectrum disorder revealed promising results comparable to the typically developing group. Finally, this dissertation investigated the necessity of renewable context for prolonged interaction with robot companions. Providing personalized tasks that match each individual's preferences and developmental stages enhances the quality of the user experience with robot learners. Confronted with the limitations of the physical workspace, this research proposes utilizing commercially available touchscreen smart devices as a shared platform for engaging the user in educational, entertainment, and therapeutic tasks with the robot learners. To summarize, this dissertation attempts to defend the thesis statement that a robot learner that utilizes an IIBL approach improves the performance and efficiency of general task learning, and when combined with the state-of-the-art mobile technology that provides personalized context for interaction, enhances the user's experience for prolonged engagement of the task.
Robot-based haptic perception and telepresence for the visually impaired

(Georgia Institute of Technology, 2012-06-28) Park, Chung Hyuk

With the advancements in medicine and welfare systems, the average life span of modern human beings is expanding, creating a new market for elderly care and assistive technology. Along with the development of assistive devices based on traditional aids such as voice-readers, electronic wheelchairs, and prosthetic limbs, a robotic platform is one of the most suitable platforms for providing multi-purpose assistance in human life. This research focuses on the transference of environmental perception to a human user through the use of interactive multi-modal feedback and an assistive robotic platform. A novel framework for haptic telepresence is presented to solve the problem, and state-of-the-art methodologies from computer vision, haptics, and robotics are utilized. The objective of this research is to design a framework that achieves the following: 1) This framework integrates visual perception from heterogeneous vision sensors, 2) it enables real-time interactive haptic representation of the real world through a mobile manipulation robotic platform and a haptic interface, and 3) it achieves haptic fusion of multiple sensory modalities from the robotic platform and provides interactive feedback to the human user. Specifically, a set of multi-disciplinary algorithms such as stereo-vision processes, three-dimensional (3D) map-building algorithms, and virtual-proxy based haptic volume representation processes will be integrated into a unified framework to successfully accomplish the goal. The application area of this work is focused on, but not limited to, assisting people with visual impairment with a robotic platform by providing multi-modal feedback of the environment.
Science-centric sampling approaches of geo-physical environments for realistic robot navigation

(Georgia Institute of Technology, 2012-06-20) Parker, Lonnie Thomas

The objective of this research effort is to provide a methodology for assessing the effectiveness of sampling techniques used to gather different types of geo-physical information by a robotic agent. We focus on assessing how well unique real-time sampling strategies acquire information that is, otherwise, too dangerous or costly to collect by human scientists. Traditional sampling strategies and informed search tech- niques provide the underlying structure for a navigating robotic surveyor whose goal is to collect samples that yield an accurate representation of the measured phenomena under realistic constraints. These sampling strategies are alternative improvements that provide greater information gain than current sampling technology allows. The contributions of this work include the following: 1) A method for estimating spa- tially distributed phenomena, using a partial sample set of information, that shows improvement over that of a more traditional estimation method. 2) A method for sampling this phenomena in the form of a navigation scheme for a mobile robotic survey system. 3) A method of ranking and comparing different navigation algorithms relative to one another based on performance (reconstruction error) and resource (distance) constraints. We introduce a specific class of navigation algorithms as example sampling strategies to demonstrate how our methodology allows different robot navigation options to be contrasted and the most practical strategy selected.
Adaptation of task-aware, communicative variance for motion control in social humanoid robotic applications

(Georgia Institute of Technology, 2012-01-17) Gielniak, Michael Joseph

An algorithm for generating communicative, human-like motion for social humanoid robots was developed. Anticipation, exaggeration, and secondary motion were demonstrated as examples of communication. Spatiotemporal correspondence was presented as a metric for human-like motion, and the metric was used to both synthesize and evaluate motion. An algorithm for generating an infinite number of variants from a single exemplar was established to avoid repetitive motion. The algorithm was made task-aware by including the functionality of satisfying constraints. User studies were performed with the algorithm using human participants. Results showed that communicative, human-like motion can be harnessed to direct partner attention and communicate state information. Furthermore, communicative, human-like motion for social robots produced by the algorithm allows humans partners to feel more engaged in the interaction, recognize motion earlier, label intent sooner, and remember interaction details more accurately.
Visual arctic navigation: techniques for autonomous agents in glacial environments

(Georgia Institute of Technology, 2011-06-15) Williams, Stephen Vincent

Arctic regions are thought to be more sensitive to climate change fluctuations, making weather data from these regions more valuable for climate modeling. Scientists have expressed an interest in deploying a robotic sensor network in these areas, minimizing the exposure of human researchers to the harsh environment, while allowing dense, targeted data collection to commence. For any such robotic system to be successful, a certain set of base navigational functionality must be developed. Further, these navigational algorithms must rely on the types of low-cost sensors that would be viable for use in a multi-agent system. A set of vision-based processing techniques have been proposed, which augment current robotic technologies for use in glacial terrains. Specifically, algorithms for estimating terrain traversability, robot localization, and terrain reconstruction have been developed which use data collected exclusively from a single camera and other low-cost robotic sensors. For traversability assessment, a custom algorithm was developed that uses local scale surface texture to estimate the terrain slope. Additionally, a horizon line estimation system has been proposed that is capable of coping with low-contrast, ambiguous horizons. For localization, a monocular simultaneous localization and mapping (SLAM) filter has been fused with consumer-grade GPS measurements to produce full robot pose estimates that do not drift over long traverses. Finally, a terrain reconstruction methodology has been proposed that uses a Gaussian process framework to incorporate sparse SLAM landmarks with dense slope estimates to produce a single, consistent terrain model. These algorithms have been tested within a custom glacial terrain computer simulation and against multiple data sets acquired during glacial field trials. The results of these tests indicate that vision is a viable sensing modality for autonomous glacial robotics, despite the obvious challenges presented by low-contrast glacial scenery. The findings of this work are discussed within the context of the larger arctic sensor network project, and a direction for future work is recommended.
Automatic coordination and deployment of multi-robot systems

(Georgia Institute of Technology, 2009-03-31) Smith, Brian Stephen

We present automatic tools for configuring and deploying multi-robot networks of decentralized, mobile robots. These methods are tailored to the decentralized nature of the multi-robot network and the limited information available to each robot. We present methods for determining if user-defined network tasks are feasible or infeasible for the network, considering the limited range of its sensors. To this end, we define rigid and persistent feasibility and present necessary and sufficient conditions (along with corresponding algorithms) for determining the feasibility of arbitrary, user-defined deployments. Control laws for moving multi-robot networks in acyclic, persistent formations are defined. We also present novel Embedded Graph Grammar Systems (EGGs) for coordinating and deploying the network. These methods exploit graph representations of the network, as well as graph-based rules that dictate how robots coordinate their control. Automatic systems are defined that allow the robots to assemble arbitrary, user-defined formations without any reliance on localization. Further, this system is augmented to deploy these formations at the user-defined, global location in the environment, despite limited localization of the network. The culmination of this research is an intuitive software program with a Graphical User Interface (GUI) and a satellite image map which allows users to enter the desired locations of sensors. The automatic tools presented here automatically configure an actual multi-robot network to deploy and execute user-defined network tasks.