Research line
PERCEPTION AND MANIPULATION
- Learning by demonstration
- Planning for perception and manipulation
- Perception of rigid and non-rigid objects
- Multimodal news article analysis
- Benchmarking and evaluation
- Knowledge representation and ontologies
- Interaction Control
- Adaptive Human-Robot Collaboration
- Virtual Reality Framework For Fast Dataset Creation
- Automatic Learning of Cognitive Exercises for Socially Assistive Robotics
- Introducing CARESSER: a Framework for in Situ Learning Robot Social Assistance from Expert Knowledge and Demonstrations
- Learning force-based multitrajectory tasks
- Active learning of manipulation sequences
- Learning safe policies in model-based active learning
- Learning collaborative behaviors
- Robot Motion Learning
- A Friction-Model-Based Framework for Reinforcement Learning of Robotic Tasks in Non-Rigid Environments
- Dual REPS: A Generalization of Relative Entropy Policy Search exploiting bad experiences
- Robot Adaptation through User Intervention and Reinforcement Learning
- Evaluation of an Interactive Learning Framework for Robot Manipulators
- Demonstration-Free Contextualized Probabilistic Movement Primitives, further enhanced with Obstacle Avoidance
- Dimensionality Reduction and Motion Coordination in Learning Trajectories with Dynamic Movement Primitives
- DR in Learning GMM of MP for Contextualized Action selection and Adaptation
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