35 citations · 52 across the 13 of their papers we have counts for
6 papers · 1 filter
HERB: Human-augmented Efficient Reinforcement learning for Bin-packing
Gojko Perovic, Nuno Ferreira Duarte, Atabak Dehban +3
Packing objects efficiently is a fundamental problem in logistics, warehouse automation, and robotics. When dealing with highly diverse 3D objects (household or grocery items), clo…
Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RL
Manuel Serra Nunes, Atabak Dehban, Yiannis Demiris +1
Despite the significant advances in Deep Reinforcement Learning (RL) observed in the last decade, the amount of training experience necessary to learn effective policies remains on…
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…
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…
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…