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Found 24 papers, 4 papers with code
Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors
To illustrate the benefits of using semantic information in scene exploration and visual search tasks, we compare its performance against traditional saliency-based models.
Gaussian Mixture Models for Affordance Learning using Bayesian Networks
Affordances are fundamental descriptors of relationships between actions, objects and effects.
Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises
In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation.
Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification
Biometrics plays a significant role in vision-based surveillance applications.
3DSGrasp: 3D Shape-Completion for Robotic Grasp
However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses.
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
Active Gaze Control for Foveal Scene Exploration
Active perception and foveal vision are the foundations of the human visual system.
Weakly-supervised fire segmentation by visualizing intermediate CNN layers
Fire localization in images and videos is an important step for an autonomous system to combat fire incidents.
Attention on Classification for Fire Segmentation
The network is jointly trained for both segmentation and classification, leading to improvement in the performance of the single-task image segmentation methods, and the previous methods proposed for fire segmentation.
Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction.
One-shot action recognition in challenging therapy scenarios
We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios.
Ranked #4 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots
We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy.
Online Body Schema Adaptation through Cost-Sensitive Active Learning
The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy
The research of a socially assistive robot has a potential to augment and assist physical therapy sessions for patients with neurological and musculoskeletal problems (e. g. stroke).
Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment
Rehabilitation assessment is critical to determine an adequate intervention for a patient.
Attention Filtering for Multi-person Spatiotemporal Action Detection on Deep Two-Stream CNN Architectures
Our method, using fovea attention filtering and our generalized binary loss, achieves a relative video mAP improvement of 20% over the two-stream baseline in AVA, and is competitive with the state-of-the-art in the UCF101-24.
Weighted Multisource Tradaboost
In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data.
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach
In this paper, a robot is taught to perform two different cleaning tasks over a table, using a learning from demonstration paradigm.
Beyond the Self: Using Grounded Affordances to Interpret and Describe Others' Actions
It then uses this information to learn a mapping between its own actions and those performed by a human in a shared environment.
Applying Domain Randomization to Synthetic Data for Object Category Detection
Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields.
Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots
One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i. e., the ability to perceive object affordances.
Language Bootstrapping: Learning Word Meanings From Perception-Action Association
The model is based on an affordance network, i. e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects.
Interactive Robot Learning of Gestures, Language and Affordances
A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots.
Unscented Bayesian Optimization for Safe Robot Grasping
We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space.
