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Institute for Robotics and Intelligent Machines (IRIM)

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Now showing 1 - 10 of 21
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Robotic Nudges: The Ethics of Engineering a More Socially Just Human Being

2015-03 , Borenstein, Jason , Arkin, Ronald C.

The time is nearing when robots are going to become a pervasive feature of our personal lives. They are already continuously operating in industrial, domestic, and military sectors. But a facet of their operation that has not quite reached its full potential is their involvement in our day-to-day routines as servants, caregivers, companions, and perhaps friends. It is clear that the multiple forms of robots already in existence and in the process of being designed will have a profound impact on human life. In fact, the motivation for their creation is largely shaped by their ability to do so. Encouraging patients to take medications, enabling children to socialize, and protecting the elderly from hazards within a living space is only a small sampling of how they could interact with humans. Their seemingly boundless potential stems in part from the possibility of their omnipresence but also because they can be physically instantiated, i.e., they are embodied in the real world, unlike many other devices. The extent of a robot’s influence on our lives hinges in large part on which design pathway the robot’s creator decides to pursue . The principal focus of this article is to generate discussion about the ethical acceptability of allowing designers to construct companion robots that nudge a user in a particular behavioral direction (and if so, under which circumstances). More specifically, we will delineate key issues related to the ethics of designing robots whose deliberate purpose is to nudge human users towards displaying greater concern for their fellow human beings, including by becoming more socially just. Important facets of this discussion include whether a robot’s “nudging ” behavior should occur with or without the user’s awareness and how much control the user should exert over it.

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Generating Human-like Motion for Robots

2013-07 , Gielniak, Michael J. , Liu, C. Karen , Thomaz, Andrea L.

Action prediction and fluidity are key elements of human-robot teamwork. If a robot’s actions are hard to understand, it can impede fluid HRI. Our goal is to improve the clarity of robot motion by making it more humanlike. We present an algorithm that autonomously synthesizes human-like variants of an input motion. Our approach is a three stage pipeline. First we optimize motion with respect to spatio-temporal correspondence (STC), which emulates the coordinated effects of human joints that are connected by muscles. We present three experiments that validate that our STC optimization approach increases human-likeness and recognition accuracy for human social partners. Next in the pipeline, we avoid repetitive motion by adding variance, through exploiting redundant and underutilized spaces of the input motion, which creates multiple motions from a single input. In two experiments we validate that our variance approach maintains the human-likeness from the previous step, and that a social partner can still accurately recognize the motion’s intent. As a final step, we maintain the robot’s ability to interact with it’s world by providing it the ability to satisfy constraints. We provide experimental analysis of the effects of constraints on the synthesized human-like robot motion variants.

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A Visualization Framework for Team Sports Captured using Multiple Static Cameras

2013 , Hamid, Raffay , Kumar, Ramkrishan , Hodgins, Jessica K. , Essa, Irfan

We present a novel approach for robust localization of multiple people observed using a set of static cameras. We use this location information to generate a visualization of the virtual offside line in soccer games. To compute the position of the offside line, we need to localize players' positions, and identify their team roles. We solve the problem of fusing corresponding players' positional information by finding minimum weight K-length cycles in a complete K-partite graph. Each partite of the graph corresponds to one of the K cameras, whereas each node of a partite encodes the position and appearance of a player observed from a particular camera. To find the minimum weight cycles in this graph, we use a dynamic programming based approach that varies over a continuum from maximally to minimally greedy in terms of the number of graph-paths explored at each iteration. We present proofs for the efficiency and performance bounds of our algorithms. Finally, we demonstrate the robustness of our framework by testing it on 82,000 frames of soccer footage captured over eight different illumination conditions, play types, and team attire. Our framework runs in near-real time, and processes video from 3 full HD cameras in about 0.4 seconds for each set of corresponding 3 frames.

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Multi-Cue Contingency Detection

2012-04 , Lee, Jinhan , Chao, Crystal , Bobick, Aaron F. , Thomaz, Andrea L.

The ability to detect a human's contingent response is an essential skill for a social robot attempting to engage new interaction partners or maintain ongoing turn-taking interactions. Prior work on contingency detection focuses on single cues from isolated channels, such as changes in gaze, motion, or sound.We propose a framework that integrates multiple cues for detecting contingency from multimodal sensor data in human-robot interaction scenarios. We describe three levels of integration and discuss our method for performing sensor fusion at each of these levels. We perform a Wizard-of-Oz data collection experiment in a turn-taking scenario in which our humanoid robot plays the turn-taking imitation game “Simon says" with human partners. Using this data set, which includes motion and body pose cues from a depth and color image and audio cues from a microphone, we evaluate our contingency detection module with the proposed integration mechanisms and show gains in accuracy of our multi-cue approach over single-cue contingency detection. We show the importance of selecting the appropriate level of cue integration as well as the implications of varying the referent event parameter.

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SLAM-Based Spatial Memory for Behavior-Based Robots

2015 , Jiang, Shu , Arkin, Ronald C.

Knowledge is essential for an autonomous robot to act intelligently when tasked with a mission. With recent leaps of progress, the paradigm of SLAM (Simultaneous Localization and Mapping) has emerged as an ideal source of spatial knowledge for autonomous robots. However, despite advancements in both paradigms of SLAM and robot control, research in the integration of these areas has been lacking and remained open to investigation. This paper presents an integration of SLAM into a behavior-based robotic system as a dynamically acquired spatial memory, which can be used to enable new behaviors and augment existing ones. The effectiveness of the integrated system is demonstrated with a biohazard search mission, where a robot is tasked to search and locate a biohazard within an unknown environment under a time constraint.

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Autonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors

2013-04 , Wu, Allen D. , Johnson, Eric N. , Kaess, Michael , Dellaert, Frank , Chowdhary, Girish

A vision-aided inertial navigation system that enables autonomous flight of an aerial vehicle in GPS-denied environments is presented. Particularly, feature point information from a monocular vision sensor are used to bound the drift resulting from integrating accelerations and angular rate measurements from an Inertial Measurement Unit (IMU) forward in time. An Extended Kalman filter framework is proposed for performing the tasks of vision-based mapping and navigation separately. When GPS is available, multiple observations of a single landmark point from the vision sensor are used to estimate the point’s location in inertial space. When GPS is not available, points that have been sufficiently mapped out can be used for estimating vehicle position and attitude. Simulation and flight test results of a vehicle operating autonomously in a simplified loss-of-GPS scenario verify the presented method.

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Lethal Autonomous Systems and the Plight of the Non-combatant

2013 , Arkin, Ronald C.

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Concurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing

2014 , Williams, Stephen , Indelman, Vadim , Kaess, Michael , Roberts, Richard , Leonard, John J. , Dellaert, Frank

We present a parallelized navigation architecture that is capable of running in real-time and incorporating long-term loop closure constraints while producing the optimal Bayesian solution. This architecture splits the inference problem into a low-latency update that incorporates new measurements using just the most recent states (filter), and a high-latency update that is capable of closing long loops and smooths using all past states (smoother). This architecture employs the probabilistic graphical models of Factor Graphs, which allows the low-latency inference and high-latency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the full joint density is employed that allows the different inference operations to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous nature of this algorithm, updates to the state estimates from the high-latency smoother will naturally be delayed until the smoother calculations have completed. This architecture has been tested within a simulated aerial environment and on real data collected from an autonomous ground vehicle. In all cases, the concurrent architecture is shown to recover the full batch solution, even while updated state estimates are produced in real-time.

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A Software Tool for the Design of Critical Robot Missions with Performance Guarantees

2013-03 , Lyons, Damian M. , Arkin, Ronald C. , Nirmal, Paramesh , Jiang, Shu. , Liu, Tsung-Ming

Deploying a robot as part of a counter-weapons of mass destruction mission demands that the robotic software operates with high assurance. A unique feature of robotic software development is the need to perform predictably in a physical environment that may only be poorly characterized in advance. In this paper, we present an approach to building high assurance software for robot missions carried out in uncertain environments. The software development framework and the verification algorithm, VIPARS, are described in detail. Results are presented for missions including motion and sensing uncertainty, interaction with obstacles, and the use of sensors to guide behavior.

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Keyframe-based Learning from Demonstration Method and Evaluation

2012-06 , Akgun, Baris , Cakmak, Maya , Jiang, Karl , Thomaz, Andrea L.

We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a human-robot interaction perspective. Our approach –Keyframe-based Learning from Demonstration (KLfD)– takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill.

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