169,161 research outputs found

    Behavior recognition for Learning from Demonstration

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    Task-Oriented Manipulation Planning: Teaching Robot Manipulators to Learn Trajectory Tasks

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    As robot manipulator applications are conducted in more complex tasks and unstructured environments, traditional manual programming cannot match the growing requirements. However, human experts usually know how to operate robot manipulators to complete tasks, but they do not know how to manually program the robot for automatically executing tasks. From a general point of view, a robot manipulation task is composed of a series of consecutive robot actions and environment states which we call it trajectory task. Imitation learning, an emerging and popular technique of robot behavior programming, is a good way to tackle this line of work but still needs robotic and machine learning skills. Moreover, the state-of-the-art methods, such as inverse reinforcement learning methods, curriculum learning methods, and behavior cloning methods are suffering from expensive data collection, intensive data labeling, target goal recognition, and sometimes combinations of those challenges. In order to solve these challenges, we propose a method that can teach robot manipulators a variety of tasks without too many robotic and machine learning skills required. Besides the drawbacks of the state-of-the-art methods, there are new challenges: noisy demonstration data, a limited number of demonstration episodes, and a low random exploring success rate. To tackle this problem, we disassembled it into three parts: demonstration episode evaluation, demonstration guided trajectory generation and utilizing vision sensors for trajectory generation. These three parts correspond to chapters 2, 3, and 4 which state the details of each challenge. From the results, our proposed method outperforms the state-of-the-art methods and can be applied to different tasks

    Speech Emotion Recognition System using Librosa for Better Customer Experience

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    Call center employees usually depend on instinct to judge a potential customer and how to pitch to them. In this paper, we pitch a more effective way for call center employees to generate more leads and engagement to generate higher revenue by analyzing the speech of the target customer by using machine learning practices and depending on data to make data-driven decisions rather than intuition. Speech Emotion Recognition otherwise known as SER is the demonstration of aspiring to perceive human inclination along with the behavior. Normally voice reflects basic feeling through tone and pitch. According to human behavior, many creatures other than human beings are also synced themselves. In this paper, we have used a python-based library named Librosa for examining music tones and sounds or speeches. In this regard, various libraries are being assembled to build a detection model utilizing an MLP (Multilayer Perceptron) classifier. The classifier will train to perceive feeling from multiple sound records. The whole implementation will be based on an existing Kaggle dataset for speech recognition. The training set will be treated to train the perceptron whereas the test set will showcase the accuracy of the model

    Discovery and recognition of motion primitives in human activities

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    We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the `motion flux', a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis

    CompILE: Compositional Imitation Learning and Execution

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    We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demonstration data. CompILE uses a novel unsupervised, fully-differentiable sequence segmentation module to learn latent encodings of sequential data that can be re-composed and executed to perform new tasks. Once trained, our model generalizes to sequences of longer length and from environment instances not seen during training. We evaluate CompILE in a challenging 2D multi-task environment and a continuous control task, and show that it can find correct task boundaries and event encodings in an unsupervised manner. Latent codes and associated behavior policies discovered by CompILE can be used by a hierarchical agent, where the high-level policy selects actions in the latent code space, and the low-level, task-specific policies are simply the learned decoders. We found that our CompILE-based agent could learn given only sparse rewards, where agents without task-specific policies struggle.Comment: ICML (2019

    Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces

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    To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201

    Prediction of intent in robotics and multi-agent systems.

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    Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches
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