arXiv subjects

Carolina Lopes

Publications and source records attributed to Carolina Lopes.

3 recordsLinked to original sources

arXiv2026

Teleoperation System for Service Robots using Virtual Reality Headset and Pose Estimation

This paper presents a teleoperation system for service robots that integrates human pose estimation with virtual reality (VR) to enable intuitive remote control. By using the Mediapipe framework for 2D pose detection and extending it with RGB-D data from the Intel RealSense D455 camera, a comprehensive 3D human pose estimation is achieved. This data is then mapped to the robot's kinematics, allowing the operator to control the robot's movements naturally. A VR interface was developed to provide an immersive perspective, enhancing the operator's situational awareness. Real-world experiments were conducted using the CHARMIE service robot to validate the system's effectiveness in tasks involving object manipulation. While the current system successfully demonstrates the potential of VR-based teleoperation, challenges remain in accurately capturing certain joint angles (e.g., wrist yaw) and ensuring precise mapping between human and robot movements. Future work includes the integration of inertial measurement units (IMUs) for improved rotational tracking and the implementation of more advanced control algorithms. Overall, this research contributes to the development of more intuitive and effective teleoperation systems for service robots, facilitating their deployment in complex and dynamic environments. This work further introduces a novel approach to teleoperation by combining RGB-D sensing with an innovative algorithm for real-time joint angle computation using Mediapipe, enhancing the precision of human-robot motion mapping and enabling immersive control through a VR interface. These contributions represent significant advancements in the field of human-robot interaction, particularly for service robot applications.

cs.CV↗

arXiv2026

Computer Vision Navigation for an Autonomous Vehicle Model Using Reinforcement Learning

This work aims to assess the capabilities of the reinforcement learning algorithm, Proximal Policy Optimization (PPO), in developing a policy capable of controlling the speed and steering angle of a vehicle model to navigate within simulated road lines. The control system's input is a camera, and its outputs are the rotational speed of the rear wheels and the steering angle of the front wheels, controlling the vehicle's speed and direction, respectively. The algorithm is trained and tested in a simulated environment, where it receives image frames from the vehicle's camera, processes them through a feature extraction algorithm, and feeds them into the policy. For each frame, the policy outputs speed and direction commands for the vehicle. The policy is iteratively updated to enhance its performance. The reinforcement learning algorithm successfully generates a policy capable of navigating the vehicle in a simplified environment. Potential improvements are suggested to enhance the system's robustness in more complex scenarios. Creating a simulation environment suitable for testing an advanced reinforcement learning algorithm is a complex task. This project offers a framework for other developers to test different approaches and algorithms. The tested algorithm, PPO, uses proximity intervals - an innovative approach in reinforcement learning. In some scenarios, the algorithm achieved better performance than predecessor algorithms, which are typically harder to implement and require tuning more hyperparameters.

cs.CV↗

arXiv2021

Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL

The amount of information produced, whether by newspapers, blogs and social networks, or by monitoring systems, is increasing rapidly. Processing all this data in real-time, while taking into consideration advanced knowledge about the problem domain, is challenging, but required in scenarios where assessing potential risks in a timely fashion is critical. C-SPARQL, a language for continuous queries over streams of RDF data, is one of the more prominent approaches in stream reasoning that provides such continuous inference capabilities over dynamic data that go beyond mere stream processing. However, it has been shown that, in the presence of huge amounts of data, C-SPARQL may not be able to answer queries in time, in particular when the frequency of incoming data is higher than the time required for reasoning with that data. In this paper, we investigate whether reasoning with C-SPARQL can be approximated using Recurrent Neural Networks and Convolutional Neural Networks, two neural network architectures that have been shown to be well-suited for time series forecasting and time series classification, to leverage on their higher processing speed once the network has been trained. We consider a variety of different kinds of queries and obtain overall positive results with high accuracies while improving processing time often by several orders of magnitude.

cs.AI↗