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In this paper, we revisit a real-time socially-aware navigation planner which helps a mobile robot to navigate alongside humans in a socially acceptable manner. This navigation planner is a modification of nav core package of Robot Operating System (ROS), based upon earlier work and further modified to use only egocentric sensors. The planner can be utilized to provide safe as well as socially appropriate...
This paper discusses the possibility of using the geometric shape of a pedestrian's head to shoulder region as a feature to identify individuals. The main aim is to track humans in a high density crowd using a 3D laser range finder (3D-LRF). One of the most critical problems of human tracking in a high density crowd is the occurrence of occlusion. Occlusion might occur because of the lack of features...
This paper describes visual route following for a cliff-climbing, tethered mobile robot for the purpose of autonomously traversing extreme terrain in the presence of obstacles. When the robot's tether contacts an obstacle, an intermediate anchor is formed. In order to detach from intermediate anchors and avoid entanglement, the robot must backtrack along its outgoing trajectory. We use the Visual...
Localization technique is a key technology for vehicular navigation and control. The use of satellite navigation is deemed essential. Yet, satellite-based navigation system is subject to multipath effect and signal blockages in urban area. To enhance the overall navigation capability, odometry techniques have been used as dead-reckoning devices to augment satellite navigation. To account the issues...
In this paper, we present a new approach for dynamic hand gesture recognition that uses intensity, depth, and skeleton joint data captured by Kinect sensor. This method integrates global and local information of a dynamic gesture. First, we represent the skeleton 3D trajectory in spherical coordinates. Then, we select the most relevant points in the hand trajectory with our proposed method for keyframe...
Currently, robot learning of human activities is mainly studied in two largely disconnected domains: high level semantics understanding in human activity recognition, and low level motion trajectory reproduction in robot imitation learning. The critical problem of human activity unified learning (HAUL) was not well studied in previous work. One important challenge is the lack of a representation that...
Robot learning frameworks, such as Programming by Demonstration, are based on learning tasks from sets of user demonstrations. These frameworks, in their naive implementation, assume that all the data from the user demonstrations has been correctly sensed and can be relevant to the task. Analogous to feature selection, which is the process of selecting a subset of relevant features for use in model...
Adaptation is an essential capability for intelligent robots to work in new environments. In the learning framework of Programming by Demonstration (PbD) and Reinforcement Learning (RL), a robot usually learns skills from a latent feature space obtained by dimension reduction techniques. Because the latent space is optimized for a specific environment during the training phase, it typically contains...
This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features...
In this paper, a robot learning approach is proposed which integrates Visuospatial Skill Learning, Imitation Learning, and conventional planning methods. In our approach, the sensorimotor skills (i.e., actions) are learned through a learning from demonstration strategy. The sequence of performed actions is learned through demonstrations using Visuospatial Skill Learning. A standard action-level planner...
This paper demonstrates some experimental proofs of the model for the classification and identification of robot sensing data. Autonomous robots are equipped with varied sensors to assist them in understanding and interacting with their environments. In contrast to traditional model approaches that are based on the Gaussian assumption, we propose the application of the infinite Gaussian mixture model...
The ability to automatically determine the road type from sensor data is of great significance for automatic annotation of routes and autonomous navigation of robots and vehicles. In this paper, we present a novel algorithm for content-based road type classification from images. The proposed method learns discriminative features from training data in an unsupervised manner, thus not requiring domain-specific...
We consider the problem of enabling a robot to autonomously open doors, including novel ones that the robot has not previously seen. Given the large variation in the appearances and locations of doors and door handles, this is a challenging perception and control problem; but this capability will significantly enlarge the range of environments that our robots can autonomously navigate through. In...
Robot learning is an efficient strategy to achieve control task without explicit programming. In this work, we present a learning framework in which the robot is taught how to perform desired behaviors through a demonstration procedure. During the demonstration, the behavior sequences are recorded and transferred to time series data, and intelligent computing methods are proposed to learn the behaviors...
We propose a methodology to characterize the modeling error of a geometric map built using straight line segments extracted from laser range finder measures. We have used a set of specific geometric shapes for the test environment in order to have ground truth to compare against the results of our line extraction approach. In these experiments, the robot executes a-priori defined navigation scripts...
In continuation of our previous work on visual, appearance-based localization in manually built maps in this paper we present a novel appearance-based, visual SLAM approach. The essential contribution of this work is, an adaptive sensor model which is estimated online and a graph matching scheme to evaluate the likelihood of a given topological map. Both methods enable the combination of an appearance-based,...
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