17 citations · 19 across the 3 of their papers we have counts for
4 papers
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in Clutter
Pooya Abolghasemi, Ladislau Bölöni
Recent research demonstrated that it is feasible to end-to-end train multi-task deep visuomotor policies for robotic manipulation using variations of learning from demonstration (L…
Pay attention! - Robustifying a Deep Visuomotor Policy through Task-Focused Attention
Pooya Abolghasemi, Amir Mazaheri, Mubarak Shah +1
Several recent studies have demonstrated the promise of deep visuomotor policies for robot manipulator control. Despite impressive progress, these systems are known to be vulnerabl…
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
Rouhollah Rahmatizadeh, Pooya Abolghasemi, Ladislau Bölöni +1
We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as w…
From virtual demonstration to real-world manipulation using LSTM and MDN
Rouhollah Rahmatizadeh, Pooya Abolghasemi, Aman Behal +1
Robots assisting the disabled or elderly must perform complex manipulation tasks and must adapt to the home environment and preferences of their user. Learning from demonstration i…