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 572

Selfie-Presentation in Everyday Life: A Large-scale Characterization of Selfie Contexts on Instagram

(Georgia Institute of Technology, 2017) Deeb-Swihart, Julia ; Polack, Christopher ; Gilbert, Eric ; Essa, Irfan

Carefully managing the presentation of self via technology is a core practice on all modern social media platforms. Recently, selfies have emerged as a new, pervasive genre of identity performance. In many ways unique, selfies bring us full-circle to Goffman—blending the online and offline selves together. In this paper, we take an empirical, Goffman-inspired look at the phenomenon of selfies. We report a large-scale, mixed-method analysis of the categories in which selfies appear on Instagram—an online community comprising over 400M people. Applying computer vision and network analysis techniques to 2.5M selfies, we present a typology of emergent selfie categories which represent emphasized identity statements. To the best of our knowledge, this is the first large-scale, empirical research on selfies. We conclude, contrary to common portrayals in the press, that selfies are really quite ordinary: they project identity signals such as wealth, health and physical attractiveness common to many online media, and to offline life.
Towards Using Visual Attributes to Infer Image Sentiment Of Social Events

(Georgia Institute of Technology, 2017) Ahsan, Unaiza ; De Choudhury, Munmun ; Essa, Irfan

Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to a variety of social events and even to unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments.
Haptic Simulation for Robot-Assisted Dressing

(Georgia Institute of Technology, 2017) Yu, Wenhao ; Kapusta, Ariel ; Tan, Jie ; Kemp, Charles C. ; Turk, Greg ; Liu, C. Karen

There is a considerable need for assistive dressing among people with disabilities, and robots have the potential to fulfill this need. However, training such a robot would require extensive trials in order to learn the skills of assistive dressing. Such training would be time-consuming and require considerable effort to recruit participants and conduct trials. In addition, for some cases that might cause injury to the person being dressed, it is impractical and unethical to perform such trials. In this work, we focus on a representative dressing task of pulling the sleeve of a hospital gown onto a person’s arm. We present a system that learns a haptic classifier for the outcome of the task given few (2-3) real-world trials with one person. Our system first optimizes the parameters of a physics simulator using real-world data. Using the optimized simulator, the system then simulates more haptic sensory data with noise models that account for randomness in the experiment. We then train hidden Markov Models (HMMs) on the simulated haptic data. The trained HMMs can then be used to classify and predict the outcome of the assistive dressing task based on haptic signals measured by a real robot’s end effector. This system achieves 92.83% accuracy in classifying the outcome of the robot-assisted dressing task with people not included in simulation optimization. We compare our classifiers to those trained on real-world data. We show that the classifiers from our system can categorize the dressing task outcomes more accurately than classifiers trained on ten times more real data.
Formal Performance Guarantees for Behavior-Based Localization Missions

(Georgia Institute of Technology, 2016-11) Lyons, Damian M. ; Arkin, Ronald C.

Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. In this paper we apply our MissionLab/VIPARS mission design and verification approach to an autonomous robot mission that uses probabilistic localization software. Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a sample-based approach which allows run-time code to be embedded in verification. Verification and experimental validation results are presented for two waypoint missions using each method, demonstrating the accuracy of verification, and both are compared with verification of an odometry-only mission, to show the mission-specific benefit of localization.
Time Dependent Control Lyapunov Functions and Hybrid Zero Dynamics for Stable Robotic Locomotion

(Georgia Institute of Technology, 2016-07) Kolathaya, Shishir ; Hereid, Ayonga ; Ames, Aaron D.

Implementing state-based parameterized periodic trajectories on complex robotic systems, e.g., humanoid robots, can lead to instability due to sensor noise exacerbated by dynamic movements. As a means of understanding this phenomenon, and motivated by field testing on the humanoid robot DURUS, this paper presents sufficient conditions for the boundedness of hybrid periodic orbits (i.e., boundedness of walking gaits) for time dependent control Lyapunov functions. In particular, this paper considers virtual constraints that yield hybrid zero dynamics with desired outputs that are a function of time or a state-based phase variable. If the difference between the phase variable and time is bounded, we establish exponential boundedness to the zero dynamics surface. These results are extended to hybrid dynamical systems, establishing exponential boundedness of hybrid periodic orbits, i.e., we show that stable walking can be achieved through time-based implementations of state-based virtual constraints. These results are verified on the bipedal humanoid robot DURUS both in simulation and experimentally; it is demonstrated that a close match between time based tracking and state based tracking can be achieved as long as there is a close match between the time and phase based desired output trajectories.
Unification of Locomotion Pattern Generation and Control Lyapunov Function-Based Quadratic Programs

(Georgia Institute of Technology, 2016-07) Chao, Kenneth Y. ; Powell, Matthew J. ; Ames, Aaron D. ; Hur, Pilwon

This paper presents a novel method of combining real-time walking pattern generation and constrained nonlinear control to achieve robotic walking under Zero-Moment Point (ZMP) and torque constraints. The proposed method leverages the fact that existing solutions to both walking pattern generation and constrained nonlinear control have been independently constructed as Quadratic Programs (QPs) and that these constructions can be related through an equality constraint on the instantaneous acceleration of the center of mass. Speci cally, the proposed method solves a single Quadratic Program which incorporates elements from Model Predictive Control (MPC) based center of mass planning methods and from rapidly exponentially stabilizing control Lyapunov function (RES-CLF) methods. The resulting QP-based controller simultaneously solves for a COM trajectory that satis es ZMP constraints over a future horizon while also producing joint torques consistent with instantaneous acceleration, torque, ZMP and RES-CLF constraints. The method is developed for simulation and experimental study on a seven-link, planar robot.
Towards Real-Time Parameter Optimization for Feasible Nonlinear Control with Applications to Robot Locomotion

(Georgia Institute of Technology, 2016-07) Powell, Matthew J. ; Ames, Aaron D.

This paper considers the application of classical control methods, designed for unconstrained nonlinear systems, to systems with nontrivial input constraints. As shown throughout the literature, unconstrained classical methods can be used to stabilize constrained systems, however, (without modification) these unconstrained methods are not guaranteed to work for a general control problem. In this paper, we propose conditions for which classical unconstrained methods can be guaranteed to exponentially stabilize constrained systems – which we term “feasibility” conditions – and we provide examples of how to construct explicitly feasible controllers. The control design methods leverage control Lyapunov functions (CLF) describing the “desired behavior” of the system; and we claim that in the event that a system’s input constraints prevent the production of an exponentially stabilizing input for a particular CLF, a new, locally feasible CLF must be produced. To this end, we propose a novel hybrid feasibility controller consisting of a continuous-time controller which implements a CLF and a discrete parameter update law which finds feasible controller parameters as needed. Simulation results suggest that the proposed method can be used to overcome certain catastrophic infeasibility events encountered in robot locomotion.
Safety Barrier Certificates for Heterogeneous Multi-Robot Systems

(Georgia Institute of Technology, 2016-07) Wang, Li ; Ames, Aaron ; Egerstedt, Magnus B.

This paper presents a formal framework for collision avoidance in multi-robot systems, wherein an existing controller is modified in a minimally invasive fashion to ensure safety. We build this framework through the use of control barrier functions (CBFs) which guarantee forward invariance of a safe set; these yield safety barrier certificates in the context of heterogeneous robot dynamics subject to acceleration bounds. Moreover, safety barrier certificates are extended to a distributed control framework, wherein neighboring agent dynamics are unknown, through local parameter identification. The end result is an optimization-based controller that formally guarantees collision free behavior in heterogeneous multi-agent systems by minimally modifying the desired controller via safety barrier constraints. This formal result is verified in simulation on a multi-robot system consisting of both “sluggish” and “agile” robots.
Correct-By-Construction Adaptive Cruise Control: Two Approaches

(Georgia Institute of Technology, 2016-07) Nilsson, Petter ; Hussien, Omar ; Balkan, Ayca ; Chen, Yuxiao ; Ames, Aaron A. D. ; Grizzle, Jessy ; Ozay, Necmiye ; Peng, Huei ; Tabuada, Paulo

Motivated by the challenge of developing control software provably meeting specifications for real world problems, this paper applies formal methods to adaptive cruise control (ACC). Starting from a Linear Temporal Logic specification for ACC, obtained by interpreting relevant ACC standards, we discuss in this paper two different control software synthesis methods. Each method produces a controller that is correct-by-construction, meaning that trajectories of the closed-loop systems provably meet the specification. Both methods rely on fixed-point computations of certain set-valued mappings. However, one of the methods performs these computations on the continuous state space whereas the other method operates on a finite-state abstraction. While controller synthesis is based on a low-dimensional model, each controller is tested on CarSim, an industry-standard vehicle simulator. Our results demonstrate several advantages over classical control design techniques. First, a formal approach to control design removes potential ambiguity in textual specifications by translating them into precise mathematical requirements. Second, because the resulting closed-loop system is known a priori to satisfy the specification, testing can then focus on the validity of the models used in control design and whether the specification captures the intended requirements. Finally, the set from where the specification (e.g., safety) can be enforced is explicitly computed and thus conditions for passing control to an emergency controller are clearly defined.
Realizing Dynamic and Efficient Bipedal Locomotion on the Humanoid Robot DURUS

(Georgia Institute of Technology, 2016-05) Reher, Jacob ; Cousineau, Eric A. ; Hereid, Ayonga ; Hubicki, Christian M. ; Ames, Aaron D.

This paper presents the methodology used to achieve efficient and dynamic walking behaviors on the prototype humanoid robotics platform, DURUS. As a means of providing a hardware platform capable of these behaviors, the design of DURUS combines highly efficient electromechanical components with “control in the loop” design of the leg morphology. Utilizing the final design of DURUS, a formal framework for the generation of dynamic walking gaits which maximizes efficiency by exploiting the full body dynamics of the robot, including the interplay between the passive and active elements, is developed. The gaits generated through this methodology form the basis of the control implementation experimentally realized on DURUS; in particular, the trajectories generated through the formal framework yield a feedforward control input which is modulated by feedback in the form of regulators that compensate for discrepancies between the model and physical system. The end result of the unified approach to control-informed mechanical design, formal gait design and regulator-based feedback control implementation is efficient and dynamic locomotion on the humanoid robot DURUS. In particular, DURUS was able to demonstrate dynamic locomotion at the DRC Finals Endurance Test, walking for just under five hours in a single day, traveling 3.9 km with a mean cost of transport of 1.61-the lowest reported cost of transport achieved on a bipedal humanoid robot.