Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

An Introduction to MultiAgent Systems.

https://www.cs.cmu.edu/~illah/PAPERS/socialroboticssurvey.pdf

A survey of socially interactive robots

Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

/pdf/human-robot-interaction-a-survey-4q8yi606a9.pdf

Human-Robot Interaction: A Survey

Service robotics is currently a highly active research area in robotics, with enormous societal potential. Since service robots directly interact with people, finding “natural” and easy-to-use user interfaces is of fundamental importance. While past work has predominately focussed on issues such as navigation and manipulation, relatively few robotic systems are equipped with flexible user interfaces that permit controlling the robot by “natural” means. This paper describes a gesture interface for the control of a mobile robot equipped with a manipulator. The interface uses a camera to track a person and recognize gestures involving arm motion. A fast, adaptive tracking algorithm enables the robot to track and follow a person reliably through office environments with changing lighting conditions. Two alternative methods for gesture recognition are compared: a template based approach and a neural network approach. Both are combined with the Viterbi algorithm for the recognition of gestures defined through arm motion (in addition to static arm poses). Results are reported in the context of an interactive clean-up task, where a person guides the robot to specific locations that need to be cleaned and instructs the robot to pick up trash.

A Gesture Based Interface for Human-Robot Interaction

This letter describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. Accurate mapping of this type of environment is challenging since the ground and the trees are surrounded by leaves, thorns and vines, and the sensor typically experiences extreme motion. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. The pipeline utilizes a custom virtual reality tool for labeling 3D scans that is used to train a semantic segmentation network. The masked point cloud is used to compute a trellis graph that identifies individual instances and extracts relevant features that are used by the SLAM module. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust, scalable, and automatically generates tree diameter estimations.

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

In many real situations, randomness is considered to be uncertainty or even confusion which impedes human beings from making a correct decision Here we study the combined role of randomness and determinism in particle dynamics for complex network community detection In the proposed model, particles walk in the network and compete with each other in such a way that each of them tries to possess as many nodes as possible Moreover, we introduce a rule to adjust the level of randomness of particle walking in the network, and we have found that a portion of randomness can largely improve the community detection rate Computer simulations show that the model has good community detection performance and at the same time presents low computational complexity

Particle competition for complex network community detection

Most of the works in the field of teleoperation of humanoid robots do not use the whole-body of the robot to perform some task. This work treats the teleoperation of a humanoid robot through the recognition of human motions by using a natural interface mechanism. We propose the use of simple mathematical techniques to allow, in a satisfactory level, the user order in real-time a robot to mimic a person, to walk, to manipulate the environment with its hands and to perform some predetermined behaviors. Also, we provide an appropriate discussion around the findings of this research. The results of the experiments show a satisfactory performance for the system developed.

Recognition of Human Motions for Imitation and Control of a Humanoid Robot

For mobile robots to assist people in everyday life, they must be easy to instruct. This paper describes a gesture-based interface for human robot interaction, which enables people to instruct robots through easy-to-perform arm gestures. Such gestures might be static pose gestures, which involve only a specific configuration of the person's arm, or they might be dynamic motion gestures (such as waving). Gestures are recognized in real-time at approximate frame rate, using a hybrid approach that integrates neural networks and template matching. A fast, color-based tracking algorithm enables the robot to track and follow a person reliably through office environments with drastically changing lighting conditions. Results are reported in the context of an interactive clean-up task, where a person guides the robot to specific locations that need to be cleaned, and the robot picks up trash which it then delivers to the nearest trash-bin.

/pdf/template-based-recognition-of-pose-and-motion-gestures-on-a-3f87arghk2.pdf

Roseli A. F. Romero

Papers

A Gesture Based Interface for Human-Robot Interaction

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Particle competition for complex network community detection

Recognition of Human Motions for Imitation and Control of a Humanoid Robot

Template-based recognition of pose and motion gestures on a mobile robot