Ameya Pore

Orcid: 0000-0001-7380-6118

According to our database1, Ameya Pore authored at least 13 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Surgical subtask automation for intraluminal procedures using deep reinforcement learning
PhD thesis, 2024

DEAR: Disentangled Environment and Agent Representations for Reinforcement Learning without Reconstruction.
CoRR, 2024

A review on machine learning in flexible surgical and interventional robots: Where we are and where we are going.
Biomed. Signal Process. Control., 2024

2023
Autonomous Navigation for Robot-Assisted Intraluminal and Endovascular Procedures: A Systematic Review.
IEEE Trans. Robotics, August, 2023

Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation.
IROS, 2023

2022
Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User Study.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

2021
Learning from Demonstrations for Autonomous Soft-tissue Retraction.
CoRR, 2021

Learning from Demonstrations for Autonomous Soft-tissue Retraction <sup>*</sup>.
Proceedings of the International Symposium on Medical Robotics, 2021

Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

Intrinsic Robotic Introspection: Learning Internal States From Neuron Activations.
Proceedings of the IEEE International Conference on Development and Learning, 2021

Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks.
Proceedings of the 20th International Conference on Advanced Robotics, 2021

2020
Soft Tissue Simulation Environment to Learn Manipulation Tasks in Autonomous Robotic Surgery<sup>*</sup>.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020


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