67 citations · 255 across the 19 of their papers we have counts for
20 papers
Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics
João Castelo-Branco, José Santos-Victor, Alexandre Bernardino
Autonomous object search is challenging for mobile robots operating in indoor environments due to partial observability, perceptual uncertainty, and the need to trade off explorati…
Gaussian Mixture Models for Affordance Learning using Bayesian Networks
Pedro Osório, Alexandre Bernardino, Ruben Martinez-Cantin +1
Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select…
Expressing and Inferring Action Carefulness in Human-to-Robot Handovers
Linda Lastrico, Nuno Ferreira Duarte, Alessandro Carfì +4
Implicit communication plays such a crucial role during social exchanges that it must be considered for a good experience in human-robot interaction. This work addresses implicit c…
Force Feedback Control For Dexterous Robotic Hands Using Conditional Postural Synergies
Dimitrios Dimou, Jose Santos-Victor, Plinio Moreno
We present a force feedback controller for a dexterous robotic hand equipped with force sensors on its fingertips. Our controller uses the conditional postural synergies framework…
3DSGrasp: 3D Shape-Completion for Robotic Grasp
Seyed S. Mohammadi, Nuno F. Duarte, Dimitris Dimou +8
Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are…
If You Are Careful, So Am I! How Robot Communicative Motions Can Influence Human Approach in a Joint Task
Linda Lastrico, Nuno Ferreira Duarte, Alessandro Carfì +4
As humans, we have a remarkable capacity for reading the characteristics of objects only by observing how another person carries them. Indeed, how we perform our actions naturally…
Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations
André Santos, Nuno Ferreira Duarte, Atabak Dehban +1
We tackle the challenge of robotic bin packing with irregular objects, such as groceries. Given the diverse physical attributes of these objects and the complex constraints governi…
SENSORIMOTOR GRAPH: Action-Conditioned Graph Neural Network for Learning Robotic Soft Hand Dynamics
João Damião Almeida, Paul Schydlo, Atabak Dehban +1
Soft robotics is a thriving branch of robotics which takes inspiration from nature and uses affordable flexible materials to design adaptable non-rigid robots. However, their flexi…
One-shot action recognition in challenging therapy scenarios
Alberto Sabater, Laura Santos, Jose Santos-Victor +3
One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approac…
Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study
Manuel Serra Nunes, Atabak Dehban, Plinio Moreno +1
A defining characteristic of intelligent systems is the ability to make action decisions based on the anticipated outcomes. Video prediction systems have been demonstrated as a sol…
Action Anticipation for Collaborative Environments: The Impact of Contextual Information and Uncertainty-Based Prediction
Clebeson Canuto, Plinio Moreno, Jorge Samatelo +2
To interact with humans in collaborative environments, machines need to be able to predict (i.e., anticipate) future events, and execute actions in a timely manner. However, the ob…
Learning Motor Resonance in Human-Human and Human-Robot Interaction with Coupled Dynamical System
Nuno Ferreira Duarte, Mirko Raković, José Santos-Victor
Human interaction involves very sophisticated non-verbal communication skills like understanding the goals and actions of others and coordinating our own actions accordingly. Neuro…
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach
Jaeseok Kim, Nino Cauli, Pedro Vicente +4
Nowadays, autonomous service robots are becoming an important topic in robotic research. Differently from typical industrial scenarios, with highly controlled environments, service…
Applying Domain Randomization to Synthetic Data for Object Category Detection
João Borrego, Atabak Dehban, Rui Figueiredo +3
Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and larg…
Automatic generation of object shapes with desired functionalities
Mihai Andries, Atabak Dehban, José Santos-Victor
3D objects (artefacts) are made to fulfill functions. Designing an object often starts with defining a list of functionalities that it should provide, also known as functional requ…
Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots
Giovanni Saponaro, Pedro Vicente, Atabak Dehban +3
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…
Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction
Paul Schydlo, Mirko Rakovic, Lorenzo Jamone +1
Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human ac…
Action Anticipation: Reading the Intentions of Humans and Robots
Nuno Ferreira Duarte, Jovica Tasevski, Moreno Coco +3
Humans have the fascinating capacity of processing non-verbal visual cues to understand and anticipate the actions of other humans. This "intention reading" ability is underpinned…
Language Bootstrapping: Learning Word Meanings From Perception-Action Association
Giampiero Salvi, Luis Montesano, Alexandre Bernardino +1
We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associati…
High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline
Laura Antanas, Plinio Moreno, Marion Neumann +4
While grasps must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the…