68 citations · 69 across the 8 of their papers we have counts for
9 papers
DEAR: Disentangled Environment and Agent Representations for Reinforcement Learning without Reconstruction
Ameya Pore, Riccardo Muradore, Diego Dall'Alba
Reinforcement Learning (RL) algorithms can learn robotic control tasks from visual observations, but they often require a large amount of data, especially when the visual scene is…
Autonomous Navigation for Robot-assisted Intraluminal and Endovascular Procedures: A Systematic Review
Ameya Pore, Zhen Li, Diego Dall'Alba +7
Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and ves…
Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation
Davide Corsi, Luca Marzari, Ameya Pore +4
The field of robotic Flexible Endoscopes (FEs) has progressed significantly, offering a promising solution to reduce patient discomfort. However, the limited autonomy of most robot…
Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User Study
Ameya Pore, Martina Finocchiaro, Diego Dall'Alba +6
Flexible endoscopes for colonoscopy present several limitations due to their inherent complexity, resulting in patient discomfort and lack of intuitiveness for clinicians. Robotic…
Learning from Demonstrations for Autonomous Soft-tissue Retraction
Ameya Pore, Eleonora Tagliabue, Marco Piccinelli +3
The current research focus in Robot-Assisted Minimally Invasive Surgery (RAMIS) is directed towards increasing the level of robot autonomy, to place surgeons in a supervisory posit…
Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery
Ameya Pore, Davide Corsi, Enrico Marchesini +4
Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This tas…
Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks
Luca Marzari, Ameya Pore, Diego Dall'Alba +3
Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the data-hungr…
Intrinsic Robotic Introspection: Learning Internal States From Neuron Activations
Nikos Pitsillos, Ameya Pore, Bjorn Sand Jensen +1
We present an introspective framework inspired by the process of how humans perform introspection. Our working assumption is that neural network activations encode information, and…
On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning
Ameya Pore, Gerardo Aragon-Camarasa
We present a behaviour-based reinforcement learning approach, inspired by Brook's subsumption architecture, in which simple fully connected networks are trained as reactive behavio…