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Timeseries sensor data processing is indispensable for system monitoring. Working with autonomous vehicles requires mechanisms that provide insightful information about the status of a mission. In a setting where time and resources are limited, trajectory classification plays a vital role in mission monitoring and failure detection. In this context, we use navigational data to interpret trajectory...
Human action recognition and generation for imitation learning are very important topic of the robot-human interaction research field. In this paper, we present a novel approach for human action recognition and robot action generation based on Kinect motion captured data using Hidden Markov Models (HMMs). The robot recognizes the captured human actions using HMMs, and generates the similar actions...
We present an approach to learn and generate movements for robot actions from human demonstrations using Dynamical Movement Primitives (DMPs) framework. The human hand movements are recorded by a motion tracker using a Kinect sensor with a color-marker glove. We segment an observed movement into simple motion units which are called as motion primitives. Then, each motion primitive will be encoded...
Learning to perform tasks like pulling a door handle or pushing a button, inherently easy for a human, can be surprisingly difficult for a robot. A crucial problem in these kinds of in-contact tasks is the context specificity of pose and force requirements. In this paper, a robot learns in-contact tasks from human kinesthetic demonstrations. To address the need to balance between the position and...
We present a system for learning haptic affordance models of complex manipulation skills. The goal of a haptic affordance model is to improve task completion by characterizing the feel of a particular object-action pair. We use learning from demonstration to provide the robot with an example of a successful interaction with a given object. We then use environmental scaffolding and a wrist-mounted...
We introduce a new method for robots to further improve upon skills acquired through Learning from Demonstration. Previously, we have introduced a method to learn both an action model to execute the skill and a goal model to monitor the execution of the skill. In this paper we show how to use the learned goal models to improve the learned action models autonomously, without further user interaction...
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...
Robots navigating in a social way should use some knowledge about common motion patterns of people in the environment. Moreover, it is known that people move intending to reach certain points of interest, and machine learning techniques have been widely used for acquiring this knowledge by observation. Learning algorithms such as Growing Hidden Markov Models (GHMMs) usually assume that points of interest...
Learning motor skills from multiple demonstrations presents a number of challenges. One of those challenges is the occurrence of occlusions and lack of sensor coverage, which may corrupt part of the recorded data. Another issue is the variability in speed of execution of the demonstrations, which may require a way of finding the correspondence between the time steps of the different demonstrations...
We present a framework for generating trajectories of the hand movement during manipulation actions from demonstrations so the robot can perform similar actions in new situations. Our contribution is threefold: 1) we extract and transform hand movement trajectories using a state-of-the-art markerless full hand model tracker from Kinect sensor data; 2) we develop a new bio-inspired trajectory segmentation...
We present a motion planning approach for performing a learned task while avoiding obstacles and reacting to the movement of task-relevant objects. We employ a closed-loop sampling-based motion planner that acquires new sensor information, generates new collision-free plans that are based on a learned task model, and replans at an average rate of more than 10 times per second for a 7-DOF manipulator...
Phase transitions in manipulation tasks often occur when contacts between objects are made or broken. A switch of the phase can result in the robot's actions suddenly influencing different aspects of its environment. Therefore, the boundaries between phases often correspond to constraints or subgoals of the manipulation task. In this paper, we investigate how the phases of manipulation tasks can be...
Hidden Markov Models (HMMs) are applied to interoceptive data (in this case the sense of rotation by way of a gyroscope) acquired by a moving wheeled robot when contouring an indoor environment. We demonstrate the soundness of HMMs to solve the problem of robot localization in a topological model of the environment, particularly the kidnapped robot problem and position tracking. In this approach,...
This paper presents a novel method to recognize the human gesture using binary decision tree and Multi-class Support Vector Machine (MCSVM). In a learning stage, 3D trajectory of the human gesture by a kinect sensor is assigned into the tree node of the binary decision tree according to its distribution property. The user's gesture trajectory is resampled and normalized, and we extract the chain code...
To efficiently plan complex manipulation tasks, robots need to reason on a high level. Symbolic planning, however, requires knowledge about the preconditions and effects of the individual actions. In this work, we present a practical approach to learn manipulation skills, including preconditions and effects, based on teacher demonstrations. We believe that requiring only a small number of demonstrations...
A learning framework with a bidirectional communication channel is proposed, where a human performs several demonstrations of a task using a haptic device (providing him/her with force-torque feedback) while a robot captures these executions using only its force-based perceptive system. Our work departs from the usual approaches to learning by demonstration in that the robot has to execute the task...
The ability to accurately localize themselves is a fundamental pre-condition for service robots designed to carry out navigation and transportation tasks. Because of the high degree of dynamics in populated and real-world environments, often artificial landmarks are used to achieve the desired accuracy in localization. In this paper we consider the problem of optimally placing landmarks for robots...
The paper presents a navigation algorithm for dynamic probabilistic environments. The static environment is unknown; moving pedestrians are detected and tracked on-line. Pedestrians are supposed to move along typical motion patterns represented by HMMs. The planning algorithm is based on an extension of the rapidly-exploring random tree algorithm, where the likelihood of the obstacles future trajectory...
A novel trajectory segmentation and modeling approach is presented. Trajectory segmentation and matching is an important step in the programming by demonstration (PbD) process to extract the user's intentions from multiple trajectories. To match multiple trajectories, the segmentation and modeling approach must be consistent and robust to disparities caused by robot dynamics and human imperfections...
In this paper, a novel method to synthesize a desired trajectory and sensory feedback control laws for robots based on the statistical characteristics of direct teaching data by a human is proposed. This work was motivated by a poor performance of an origami-folding robot developed by the authors. Since the robot simply replayed a given trajectory without sensory feedback control, it often failed...
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