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In the case of human intervention in disaster response operations like indoor firefighting, where the environment perception is limited due to thick smoke, noise in the oxygen masks and clutter, not only limit the environmental perception of the human responders, but also causes distress. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance...
We evaluated the effects of robot gaze behavior on interactions with multiple users in a museum-like setting. We posit that a robot needs to divide its attention between multiple users and may be able to use its gaze to ‘point’ at objects of interest. A 2 (person-oriented [only looking at participants] vs. object-oriented [also looking at artworks] gaze) × 2 (‘favored’ [looked at more] vs. ‘not favored’...
Cooperation1 is at the core of human social life. In this context, two major challenges face research on humanrobot interaction: the first is to understand the underlying structure of cooperation, and the second is to build, based on this understanding, artificial agents that can successfully and safely interact with humans. Here we take a psychologically grounded and human-centered approach that...
We present a multimodal system for creating, modifying and commanding groups of robots from a population. Extending our previous work on selecting an individual robot from a population by face engagement, we show that we can dynamically create groups of a desired number of robots by speaking the number we desire, e.g. “You three”, and looking at the robots we intend to form the group. We evaluate...
We present a methodology for enabling service robots to follow natural language commands from non-expert users, with and without user-specified constraints, with a particular focus on spatial language understanding. As part of our approach, we propose a novel extension to the semantic field model of spatial prepositions that enables the representation of dynamic spatial relations involving paths....
Motion recognition is an essential technology for social robots in various environments such as homes, offices and shopping center, where the robots are expected to understand human behavior and interact with them. In this paper, we present a system composed of three models: motion language model, natural language model and integration inference model, and achieved to generate sentences from motions...
In this study, we propose a method for concept formation and word acquisition for robots. The proposed method is based on multimodal latent Dirichlet allocation (MLDA) and the nested Pitman-Yor language model (NPYLM). A robot obtains haptic, visual, and auditory information by grasping, observing, and shaking an object. At the same time, a user teaches object features to the robot through speech,...
This paper presents a comparison of open-loop and closed-loop control strategies for tracking a task space trajectory, using redundant robots. We do not assume any knowledge of the analytical forward and inverse kinematics, relying instead on learning these models online, while executing a desired task. Specifically, we employ a recent learning algorithm that allows to learn a probabilistic model...
Given a stochastic policy learned by reinforcement, we wish to ensure that it can be deployed on a robot with demonstrably low probability of unsafe behavior. Our case study is about learning to reach target objects positioned close to obstacles, and ensuring a reasonably low collision probability. Learning is carried out in a simulator to avoid physical damage in the trial-and-error phase. Once a...
This paper presents a path-repeating, mobile robot controller that combines a feedforward, proportional Iterative Learning Control (ILC) algorithm with a feedback-linearized path-tracking controller to reduce path-tracking errors over repeated traverses along a reference path. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation...
Learning can be used to optimize robot motions to new situations. Learning motions can cause high frequency random motions in the exploration phase and can cause failure before the motion is learned. The mean time between failures (MTBF) of a robot can be predicted while it is performing these motions. The predicted MTBF in the exploration phase can be increased by filtering actions or possible actions...
This paper presents the application of reinforcement learning to improve the performance of highly dynamic single legged locomotion with compliant series elastic actuators. The goal is to optimally exploit the capabilities of the hardware in terms of maximum jump height, jump distance, and energy efficiency of periodic hopping. These challenges are tackled with the reinforcement learning method Policy...
In this paper we present a framework to learn a model-free feedback controller for locomotion and balance control of a compliant quadruped robot walking on rough terrain. Having designed an open-loop gait encoded in a Central Pattern Generator (CPG), we use a neural network to represent sensory feedback inside the CPG dynamics. This neural network accepts sensory inputs from a gyroscope or a camera,...
Robot task planning is an inherently challenging problem, as it covers both continuous-space geometric reasoning about robot motion and perception, as well as purely symbolic knowledge about actions and objects. This paper presents a novel “knowledge of volumes” framework for solving generic robot tasks in partially known environments. In particular, this approach (abbreviated, KVP) combines the power...
Autonomous vehicles (or drones) are very frequently used for servicing a geographic region in numerous applications. Given a geographic territory and a set of n fixed vehicle depots, we consider the problem of designing service districts so as to balance the workload of a collection of vehicles which service this region. We assume that the territory is a connected polygonal region, i.e. a simply connected...
Production speed and energy efficiency are crucial factors for any application scenario in industrial robotics. The most important factor for this is planning of an optimized sequence of atomic subtasks. In a welding scenario, an atomic subtask could be understood as a single welding seam/spot while the sequence could be the ordering of these atomic tasks. Optimization of a task sequence is normally...
We present a hierarchical planning and execution architecture that maintains the computational efficiencyofhierar-chical decomposition while improving optimality. It provides mechanisms for monitoring the belief state during execution and performing selective replanningtorepair poor choices and take advan-tageofnew opportunities. It also provides mechanisms for looking ahead into future planstoavoid...
While symbolic planners work with an abstract representation of the real world, allowing plans to be constructed relatively quickly, geometric planning — although more computationally complex — is essential for building symbolic plans that actually work in the real world. To combine the two types of systems, we present in this paper a meaningful interface, and insights into a methodology for developing...
We propose a novel framework to combine model-checking-based motion planning with action planning using action description languages, aiming to tackle task specifications given as Linear Temporal Logic (LTL) formulas. The specifications implicitly require both sequential regions to visit and the desired actions to perform at these regions. The robot's motion is abstracted based on sphere regions of...
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