Fundamental Research (D1)

We aim at making robotic system autonomous and bring them in real world environments and solve real-world problems. Since legislation is one of the key barriers to doing so, we also work on the development of novel teleoperation systems to assist the human operator during complex teleoperation tasks (robotic surgery, nuclear sort and segregation). This makes teleoperation a much easier and cheaper solution allowing more penetration of robotic technologies in the market.

How best to enable a robot to learn movements plan based on sensory information without hand-designed features?
How can a robot learn the motion control for complex interactive tasks?
How best we can reduce the cognitive workload on a human operator (e.g., a surgeon) during teleoperation by intelligent systems?

From theoretical point of view, my research focuses on:

Data driven motion planning and control (presented in Sanni 2022; and Mandil 2022 papers; and Tafuro 2022; and Bonvicini 2022 submissions)
Robot learning from demonstrations (Ghalamzan 2015, Ghalamzan 2018 and Osa 2017)
Haptic enabled teleoperation and (Parsa et al. 2020--Best Paper Award Finalist at IEEE CASE 2020, Parsa et al. 2022, Ghalamzan et al. 2017 and Selvagio et al. 2019);
Grasping and manipulation planning/control (Mavrakis et al. 2016--Best Interactive Paper Award Finalist at IEEE Humanoids 2016, Mavrakis et al. 2017, Ghalamzan et al. 2016, Pardi et al. 2018 and 2021),
Robot Perception (Tafuro ICRA 2022);

Robot Learning from Demonstrations (LfD)

LfD is key to bringing robots to human workspaces at scale. It also can help reduce programming time and cost so that non-experts can easily programme a robot for different tasks. The links between robot and human LfD (Ghalamzan 2015 Incremental and Ghalamzan 2018} demonstrate the available technologies are far away from the corresponding human capabilities of learning from observations. We have been working on developing LfD approaches for a wide range of applications, e.g. Breast Cancer Examination robot (Sanni 2022), Strawberry picking (Alessandra 2022), simple pick-and-place (Ghalamzan 2015 incremental and Ghalamzan 2018) and nuclear sort and segregation (Osa 2017 guiding). We are keen to develop fundamental concepts to make LfD available for non-expert users across several application domains.

Data-driven predictive models (DPM)

: Most manipulation tasks involve interaction with environments. These interactions are always challenging to the model via classical approaches; hence, they are complicated to control. We are interested in developing data-driven predictive forward models that can help a robot to interact with its environment. For instance, Nazari et al. 2021 and Willow et al. 2022 present action conditioned predictive models for tactile sensors. DPM can be used for, e.g., robust object manipulation by slip avoidance controller via model predictive control (MPC). It can be also used for cluster manipulation for picking up an object on the shelf or strawberry picking (Mghames et al. 2020). Predictive models are a core part of a robust interactive robotic task.

Reduced cognitive load teleoperation

Fully teleoperated robotic systems are the sole solutions to many problems, e.g. from robotic surgery to nuclear sort & segregation. These conservative industries do not trust a fully autonomous system even if such a system is available and needs a human operator to be in charge of the task execution. We worked on a new technology of shared control which can bridge the gap between autonomous systems and fully teleoperated systems. We proposed to use haptic force cues as a means to guide human operators. The guidance in teleoperation can provide a human operator with optimal actions in using a surgical robot (Selvaggio et al. 2019) or predicted singularities (Ghalamzan et al. 2017}, and collision (Parsa et al. 2020} and minimum required torques (Rahal et al. 2022). Haptic guidance is important in reducing the cognitive workload (Talha et al. 2016). Motion scaling is also a factor in reducing the cognitive workload (Parsa et al. 2022). We envisage an Intelligent Tele-operation System benefiting from Intelligent Autonomous System via haptic guidance or adaptive motion scaling. That can increase the penetration of robotic technologies in the market and make them cheaper solutions, e.g. it can significantly reduce the operation time of robotic surgery.

Robotic grasping and manipulation

Our work in grasping and manipulation is manifold: (1) task-informed grasping (TIG); (2) Motion planning; (3) Motion Control. The common practice of robotic grasping includes an intelligent system extracting grasp related features from RGB-D images. This alone is not enough for effective and robust robotic grasping as the synthesised grasp may not be good enough for a specific manipulator. Task-informed grasping considers robot specific objectives into consideration in selecting the grasp poses. Hence, the robot can predict the consequences of the grasp actions (e.g. singularity (Ghalamzan et al. 2016), torques (Mavrakis et al. 2016), torques (Mavrakis et al. 2017), collision (Pardi et al. 2018) and affordance (Pardi et al. 2021) during the planned manipulative movements of a follower manipulator. We also work on motion planning for interactive robotic manipulation motion planning and control are needed. For effective motion planning for robotic cutting, one should define the robot movements based on the surface of the object to be cut (Pardi et al. 2020). For robotic breast palpation (to localise the cancerous lumps) motion control and searching policy, such as RL, are required (Giorgo et al. 2022).

Manipulation Perception

Our work in Manipulation Perception is manifold: (1) Strawberry detection-segmentation and picking point localisation; (2) Active Perception; (3) Tactile Perception. We work on Detection, Segmentation, and Localisation for robotic grasping, and fruit picking (Tafuro et al. 2022). We also work on Active Perception which allows a robot to take action to optimise the perception performaence. We also use tactile sensors for the robot to make the robot aware of the interction states.

Related Publications

Published by IML members

                @article{giorgo2022,
                    title={Deep Movement Primitives and control policy for breast cancer examination robot},
                    author={Bonvicini, Giorgio and Sprida, Venkatesh and Zanchettin, Andrea and Ghalamzan, Amir},
                    journal={Robotic Automation Letter (IROS) submitted},
                    year={2022}
                  }

                @inproceedings{Mavrakis2016,
                    doi = {10.1109/humanoids.2016.7803274},
                    url = {https://doi.org/10.1109/humanoids.2016.7803274},
                    year = {2016},
                    month = nov,
                    publisher = {{IEEE}},
                    author = {Nikos Mavrakis and Amir M. Ghalamzan E. and Rustam Stolkin and Luca Baronti and Marek Kopicki and Marco Castellani},
                    title = {Analysis of the inertia and dynamics of grasped objects,  for choosing optimal grasps to enable torque-efficient post-grasp manipulations},
                    booktitle = {2016 {IEEE}-{RAS} 16th International Conference on Humanoid Robots (Humanoids)}
                }

                @inproceedings{Sanni2022,
                    title={Deep robot path planning from demonstrations for breast cancer examination},
                    author=Sanni, Oluwatoin and Bonvicini, Giorgio and Khan, Arshad, MA Khan and Lopez-Custodio, Pablo and Nazari, Kiyanoush and Ghalamzan E., Amir,
                    booktitle=Proceedings of 36th AAAI conference on Artificial Intelligence,
                    year={2022},
                    url={https://arxiv.org/pdf/2202.09265.pdf}
                }
                @inproceedings{Mandil2022,
                    title={Action Conditioned Tactile Prediction: case study on slip prediction},
                    author=Mandill, Willow and Nazari, Kiyanoush and Ghalamzan E., Amir,
                    booktitle=Proceedings of Robotics: Science and Systems (RSS),
                    year={2022},
                    url={https://arxiv.org/pdf/2202.09265.pdf}
                }
                @inproceedings{Parsal2022,
                    title={The Impact of Motion Scaling and Haptic Guidance on Operators’ Workload and Performance in Teleoperation},
                    author=Parsa, Soran and Maior, Horia and Thumwood, Alex and Wilson, Max and Ghalamzan E., Amir,
                    booktitle=CHI Conference on Human Factors in Computing Systems,
                    year={2022},
                    url={https://arxiv.org/pdf/2202.09265.pdf}
                }
                @inproceedings{Tafurol2022,
                    title={Strawberry picking pont, wieght and quality estimation},
                    author=Tafuro, Alessandra and Adewumi, Adeayo and Parsa, Soran and Ghalamzan E., Amir and Debnat, Bappaditya,
                    booktitle=IEEE Conference on Robotics and Automation (ICRA),
                    year={2022},
                    url={https://arxiv.org/pdf/2202.09265.pdf}
                }
                @inproceedings{Ghalamzanl2022,
                    title={Solar Energy Technology in Precision Agriculture and Smart Farming},
                    author=Ghalamzan E., Amir and et al.,
                    booktitle=Elsevier book on Solar Energy,
                    year={2022},
                    url={}
                }
                @inproceedings{Nazari2021,
                    title={Tactile Dynamic Behaviour Prediction Based on Robot Action},
                    author={Nazari, Kiyanoush and Mandil, Willow and Hanheide, Marc and Ghalamzan, Amir},
                    booktitle={Proceedings of TAROS 2021},
                    year={2021},
                    url={https://lcas.lincoln.ac.uk/wp/wp-content/uploads/2021/09/t5.3-TAROS2021paper18.pdf}
                }
                @inproceedings{crivellari2021deep,
                    title={Deep Robot Path Planning from Demonstrations for Breast Cancer Examination},
                    author={Crivellari, Marta and Sanni, Oluwatoyin and Zanchettin, Andrea and Esfahani, Amir Ghalamzan},
                    booktitle={Annual Conference Towards Autonomous Robotic Systems},
                    pages={260--272},
                    year={2021},
                    organization={Springer}
                  }
                @inproceedings{esfahani2021tactile,
                    title={Tactile Dynamic Behaviour Prediction Based on Robot Action},
                    author={Nazari, Kiyanoush and Mandil, Willow and Ghalamzan, Amir },
                    booktitle={Towards Autonomous Robotic Systems: 22nd Annual Conference, TAROS 2021, Lincoln, UK, September 8--10, 2021, Proceedings},
                    volume={13054},
                    pages={284},
                    year={2021},
                    organization={Springer Nature}
                }
                @article{ghalamzan2021deep,
                    title={Deep-LfD: deep robot learning from demonstrations},
                    author={Ghalamzan, Amir and Nazari, Kiyanoush and Hashempour, Hamidreza and Zhong, Fangxun},
                    journal={Software Impacts},
                    volume={9},
                    pages={100087},
                    year={2021},
                    publisher={Elsevier}
                  }

                  @article{pardi2020planning,
                    title={Planning maximum-manipulability cutting paths},
                    author={Pardi, Tommaso and Ortenzi, Valerio and Fairbairn, Colin and Pipe, Tony and Esfahani, Amir Masoud Ghalamzan and Stolkin, Rustam},
                    journal={IEEE Robotics and Automation Letters},
                    volume={5},
                    number={2},
                    pages={1999--2006},
                    year={2020},
                    publisher={IEEE}
                  }

                @inproceedings{Mavrakis2020,
                    doi = {10.1109/iros45743.2020.9341112},
                    url = {https://doi.org/10.1109/iros45743.2020.9341112},
                    year = {2020},
                    month = oct,
                    publisher = {{IEEE}},
                    author = {Nikos Mavrakis and Amir M. Ghalamzan E. and Rustam Stolkin},
                    title = {Estimating An Object's Inertial Parameters By Robotic Pushing: A Data-Driven Approach},
                    booktitle = {2020 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
                }
                @inproceedings{Parsa2020,
                    doi = {10.1109/case48305.2020.9216789},
                    url = {https://doi.org/10.1109/case48305.2020.9216789},
                    year = {2020},
                    month = aug,
                    publisher = {{IEEE}},
                    author = {Soran Parsa and Disha Kamale and Sariah Mghames and Kiyanoush Nazari and Tommaso Pardi and Aravinda R. Srinivasan and Gerhard Neumann and Marc Hanheide and Ghalamzan E. Amir},
                    title = {Haptic-guided shared control grasping: collision-free manipulation},
                    booktitle = {2020 {IEEE} 16th International Conference on Automation Science and Engineering ({CASE})}
                }
                @inproceedings{Mghames2020,
                    doi = {10.1109/iros45743.2020.9341728},
                    url = {https://doi.org/10.1109/iros45743.2020.9341728},
                    year = {2020},
                    month = oct,
                    publisher = {{IEEE}},
                    author = {Sariah Mghames and Marc Hanheide and Amir Ghalamzan E.},
                    title = {Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking},
                    booktitle = {2020 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
                }
                @inproceedings{Selvaggio2019,
                    doi = {10.1109/iros40897.2019.8968109},
                    url = {https://doi.org/10.1109/iros40897.2019.8968109},
                    year = {2019},
                    month = nov,
                    publisher = {{IEEE}},
                    author = {Mario Selvaggio and Amir M. Ghalamzan E and Rocco Moccia and Fanny Ficuciello and Bruno Siciliano},
                    title = {Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery},
                    booktitle = {2019 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
                }
                @article{GhalamzanE2018,
                    doi = {10.1016/j.robot.2017.12.001},
                    url = {https://doi.org/10.1016/j.robot.2017.12.001},
                    year = {2018},
                    month = mar,
                    publisher = {Elsevier {BV}},
                    volume = {101},
                    pages = {45--56},
                    author = {Amir M. Ghalamzan E. and Matteo Ragaglia},
                    title = {Robot learning from demonstrations: Emulation learning in environments with moving obstacles},
                    journal = {Robotics and Autonomous Systems}
                }
                @article{Osa2017,
                    doi = {10.1109/lra.2017.2653850},
                    url = {https://doi.org/10.1109/lra.2017.2653850},
                    year = {2017},
                    month = apr,
                    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
                    volume = {2},
                    number = {2},
                    pages = {819--826},
                    author = {Takayuki Osa and Amir M. Ghalamzan Esfahani and Rustam Stolkin and Rudolf Lioutikov and Jan Peters and Gerhard Neumann},
                    title = {Guiding Trajectory Optimization by Demonstrated Distributions},
                    journal = {{IEEE} Robotics and Automation Letters}
                }
                @inproceedings{Ghalamzan2017,
                    doi = {10.1109/iros.2017.8206178},
                    url = {https://doi.org/10.1109/iros.2017.8206178},
                    year = {2017},
                    month = sep,
                    publisher = {{IEEE}},
                    author = {E. Amir M. Ghalamzan and Firas Abi-Farraj and Paolo Robuffo Giordano and Rustam Stolkin},
                    title = {Human-in-the-loop optimisation: Mixed initiative grasping for optimally facilitating post-grasp manipulative actions},
                    booktitle = {2017 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
                }
                @inproceedings{Mavrakis2017,
                    doi = {10.1109/iros.2017.8206258},
                    url = {https://doi.org/10.1109/iros.2017.8206258},
                    year = {2017},
                    month = sep,
                    publisher = {{IEEE}},
                    author = {Nikos Mavrakis and E. Amir M. Ghalamzan and Rustam Stolkin},
                    title = {Safe robotic grasping: Minimum impact-force grasp selection},
                    booktitle = {2017 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
                }
                @inproceedings{Amir2017,
                    doi = {10.1109/icrom.2017.8466161},
                    url = {https://doi.org/10.1109/icrom.2017.8466161},
                    year = {2017},
                    month = oct,
                    publisher = {{IEEE}},
                    author = {M. Amir and E. Ghalamzan and Nikos Mavrakis and Rustam Stolkin},
                    title = {Grasp that optimises objectives along post-grasp trajectories},
                    booktitle = {2017 5th {RSI} International Conference on Robotics and Mechatronics ({ICRoM})}
                }
                @inproceedings{GhalamzanE2016,
                    doi = {10.1109/iros.2016.7759158},
                    url = {https://doi.org/10.1109/iros.2016.7759158},
                    year = {2016},
                    month = oct,
                    publisher = {{IEEE}},
                    author = {Amir M. Ghalamzan E. and Nikos Mavrakis and Marek Kopicki and Rustam Stolkin and Ales Leonardis},
                    title = {Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories},
                    booktitle = {2016 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
                }
                @inproceedings{Talha2016,
                    doi = {10.1109/ssrr.2016.7784294},
                    url = {https://doi.org/10.1109/ssrr.2016.7784294},
                    year = {2016},
                    month = oct,
                    publisher = {{IEEE}},
                    author = {M. Talha and E. A. M. Ghalamzan and C. Takahashi and J. Kuo and W. Ingamells and R. Stolkin},
                    title = {Towards robotic decommissioning of legacy nuclear plant: Results of human-factors experiments with tele-robotic manipulation,  and a discussion of challenges and approaches for decommissioning},
                    booktitle = {2016 {IEEE} International Symposium on Safety,  Security,  and Rescue Robotics ({SSRR})}
                }
                @inproceedings{GhalamzanE2015,
                    doi = {10.1109/icra.2015.7139467},
                    url = {https://doi.org/10.1109/icra.2015.7139467},
                    year = {2015},
                    month = may,
                    publisher = {{IEEE}},
                    author = {Amir M. Ghalamzan E. and Luca Bascetta and Marcello Restelli and Paolo Rocco},
                    title = {Estimating a Mean-Path from a set of 2-D curves},
                    booktitle = {2015 {IEEE} International Conference on Robotics and Automation ({ICRA})}
                }
                @inproceedings{GhalamzanE2015,
                    doi = {10.1109/icra.2015.7139985},
                    url = {https://doi.org/10.1109/icra.2015.7139985},
                    year = {2015},
                    month = may,
                    publisher = {{IEEE}},
                    author = {Amir M. Ghalamzan E and Chris Paxton and Gregory D. Hager and Luca Bascetta},
                    title = {An incremental approach to learning generalizable robot tasks from human demonstration},
                    booktitle = {2015 {IEEE} International Conference on Robotics and Automation ({ICRA})}
                }            
                @article{GhalamzanE2021,
                    doi = {10.1016/j.simpa.2021.100087},
                    url = {https://doi.org/10.1016/j.simpa.2021.100087},
                    year = {2021},
                    month = aug,
                    publisher = {Elsevier {BV}},
                    volume = {9},
                    pages = {100087},
                    author = {Amir Ghalamzan E. and Kiyanoush Nazari and Hamidreza Hashempour and Fangxun Zhong},
                    title = {Deep-{LfD}: Deep robot learning from demonstrations},
                    journal = {Software Impacts}
                }
                @INCOLLECTION {book,
                    author    = "Amir Ghalamzan Esfahani and Gautham P Das and Iain Gould and Payam Zarafshan and Rajendran S Vishnu and James Heselden and Amir Badiee and Isobel Wright and Simon Pearson",
                    title     = "Applications of Solar Energy in Precision Agriculture and Smart Farming",
                    booktitle = "Solar Energy Advancements in Agriculture and Food Production Systems",
                    publisher = "Elsevier",
                    year      = "In press",
                    chapter   = "10"
                }

2015

2016

2017

2018

2019

2020

2021

2022

In press

Conference

Journal

In Collection

Search:

Year	Type	Publication
In press	In Collection	Amir Ghalamzan Esfahani et al. (In press). Applications of Solar Energy in Precision Agriculture and Smart Farming. In Solar Energy Advancements in Agriculture and Food Production Systems. (bib) x @incollection{book, chapter = { 10 }, year = { In press }, publisher = { Elsevier }, booktitle = { Solar Energy Advancements in Agriculture and Food Production Systems }, title = { Applications of Solar Energy in Precision Agriculture and Smart Farming }, author = { Amir Ghalamzan Esfahani and Gautham P Das and Iain Gould and Payam Zarafshan and Rajendran S Vishnu and James Heselden and Amir Badiee and Isobel Wright and Simon Pearson }, }
2022	Journal	Giorgio Bonvicini, Venkatesh Sprida, Andrea Zanchettin, Amir Ghalamzan (2022). Deep Movement Primitives and control policy for breast cancer examination robot. Robotic Automation Letter (IROS) submitted, missing (bib) x @article{giorgo2022, year = { 2022 }, journal = { Robotic Automation Letter (IROS) submitted }, author = { Bonvicini and Sprida and Zanchettin and Ghalamzan }, title = { Deep Movement Primitives and control policy for breast cancer examination robot }, }
2022	Conference	Amir Ghalamzan E., et al. (2022). Solar Energy Technology in Precision Agriculture and Smart Farming. In Elsevier book on Solar Energy. (bib) x @inproceedings{Ghalamzanl2022, url = { }, year = { 2022 }, booktitle = { Elsevier book on Solar Energy }, author = { Ghalamzan E. and et al. }, title = { Solar Energy Technology in Precision Agriculture and Smart Farming }, }
2022	Conference	Alessandra Tafuro, Adeayo Adewumi, Soran Parsa, Amir Ghalamzan E., Bappaditya Debnat (2022). Strawberry picking pont, wieght and quality estimation. In IEEE Conference on Robotics and Automation (ICRA). (pdf) (bib) x @inproceedings{Tafurol2022, url = { https://arxiv.org/pdf/2202.09265.pdf }, year = { 2022 }, booktitle = { IEEE Conference on Robotics and Automation (ICRA) }, author = { Tafuro and Adewumi and Parsa and Ghalamzan E. and Debnat }, title = { Strawberry picking pont, wieght and quality estimation }, }
2022	Conference	Soran Parsa, Horia Maior, Alex Thumwood, Max Wilson, Amir Ghalamzan E. (2022). The Impact of Motion Scaling and Haptic Guidance on Operators’ Workload and Performance in Teleoperation. In CHI Conference on Human Factors in Computing Systems. (pdf) (bib) x @inproceedings{Parsal2022, url = { https://arxiv.org/pdf/2202.09265.pdf }, year = { 2022 }, booktitle = { CHI Conference on Human Factors in Computing Systems }, author = { Parsa and Maior and Thumwood and Wilson and Ghalamzan E. }, title = { The Impact of Motion Scaling and Haptic Guidance on Operators’ Workload and Performance in Teleoperation }, }
2022	Conference	Willow Mandill, Kiyanoush Nazari, Amir Ghalamzan E. (2022). Action Conditioned Tactile Prediction: case study on slip prediction. In Proceedings of Robotics: Science and Systems (RSS). (pdf) (bib) x @inproceedings{Mandil2022, url = { https://arxiv.org/pdf/2202.09265.pdf }, year = { 2022 }, booktitle = { Proceedings of Robotics: Science and Systems (RSS) }, author = { Mandill and Nazari and Ghalamzan E. }, title = { Action Conditioned Tactile Prediction: case study on slip prediction }, }
2022	Conference	Oluwatoin Sanni, Giorgio Bonvicini, MA Khan Khan, Pablo Lopez-Custodio, Kiyanoush Nazari, Amir Ghalamzan E. (2022). Deep robot path planning from demonstrations for breast cancer examination. In Proceedings of 36th AAAI conference on Artificial Intelligence. (pdf) (bib) x @inproceedings{Sanni2022, url = { https://arxiv.org/pdf/2202.09265.pdf }, year = { 2022 }, booktitle = { Proceedings of 36th AAAI conference on Artificial Intelligence }, author = { Sanni and Bonvicini and Khan and Lopez-Custodio and Nazari and Ghalamzan E. }, title = { Deep robot path planning from demonstrations for breast cancer examination }, }
2021	Journal	Amir Ghalamzan E., Kiyanoush Nazari, Hamidreza Hashempour, Fangxun Zhong (2021). Deep-LfD: Deep robot learning from demonstrations. Software Impacts, 9, pp. 100087 (link) (bib) x @article{GhalamzanE2021, journal = { Software Impacts }, title = { Deep-{LfD}: Deep robot learning from demonstrations }, author = { Amir Ghalamzan E. and Kiyanoush Nazari and Hamidreza Hashempour and Fangxun Zhong }, pages = { 100087 }, volume = { 9 }, publisher = { Elsevier {BV} }, month = { aug }, year = { 2021 }, url = { https://doi.org/10.1016/j.simpa.2021.100087 }, doi = { 10.1016/j.simpa.2021.100087 }, }
2021	Journal	Amir Ghalamzan, Kiyanoush Nazari, Hamidreza Hashempour, Fangxun Zhong (2021). Deep-LfD: deep robot learning from demonstrations. Software Impacts, 9, pp. 100087 (bib) x @article{ghalamzan2021deep, publisher = { Elsevier }, year = { 2021 }, pages = { 100087 }, volume = { 9 }, journal = { Software Impacts }, author = { Ghalamzan and Nazari and Hashempour and Zhong }, title = { Deep-LfD: deep robot learning from demonstrations }, }
2021	Conference	Kiyanoush Nazari, Willow Mandil, Amir Ghalamzan (2021). Tactile Dynamic Behaviour Prediction Based on Robot Action. In Towards Autonomous Robotic Systems: 22nd Annual Conference, TAROS 2021, Lincoln, UK, September 8–10, 2021, Proceedings, pp. 284. (bib) x @inproceedings{esfahani2021tactile, organization = { Springer Nature }, year = { 2021 }, pages = { 284 }, volume = { 13054 }, booktitle = { Towards Autonomous Robotic Systems: 22nd Annual Conference, TAROS 2021, Lincoln, UK, September 8--10, 2021, Proceedings }, author = { Nazari and Mandil and Ghalamzan }, title = { Tactile Dynamic Behaviour Prediction Based on Robot Action }, }
2021	Conference	Marta Crivellari, Oluwatoyin Sanni, Andrea Zanchettin, Amir Ghalamzan Esfahani (2021). Deep Robot Path Planning from Demonstrations for Breast Cancer Examination. In Annual Conference Towards Autonomous Robotic Systems, pp. 260–272. (bib) x @inproceedings{crivellari2021deep, organization = { Springer }, year = { 2021 }, pages = { 260--272 }, booktitle = { Annual Conference Towards Autonomous Robotic Systems }, author = { Crivellari and Sanni and Zanchettin and Esfahani }, title = { Deep Robot Path Planning from Demonstrations for Breast Cancer Examination }, }
2021	Conference	Kiyanoush Nazari, Willow Mandil, Marc Hanheide, Amir Ghalamzan (2021). Tactile Dynamic Behaviour Prediction Based on Robot Action. In Proceedings of TAROS 2021. (pdf) (bib) x @inproceedings{Nazari2021, url = { https://lcas.lincoln.ac.uk/wp/wp-content/uploads/2021/09/t5.3-TAROS2021paper18.pdf }, year = { 2021 }, booktitle = { Proceedings of TAROS 2021 }, author = { Nazari and Mandil and Hanheide and Ghalamzan }, title = { Tactile Dynamic Behaviour Prediction Based on Robot Action }, }
2020	Journal	Tommaso Pardi, Valerio Ortenzi, Colin Fairbairn, Tony Pipe, Amir Masoud Ghalamzan Esfahani, Rustam Stolkin (2020). Planning maximum-manipulability cutting paths. IEEE Robotics and Automation Letters, 5(2), pp. 1999–2006 (bib) x @article{pardi2020planning, publisher = { IEEE }, year = { 2020 }, pages = { 1999--2006 }, number = { 2 }, volume = { 5 }, journal = { IEEE Robotics and Automation Letters }, author = { Pardi and Ortenzi and Fairbairn and Pipe and Esfahani and Stolkin }, title = { Planning maximum-manipulability cutting paths }, }
2020	Conference	Sariah Mghames, Marc Hanheide, Amir Ghalamzan E. (2020). Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (link) (bib) x @inproceedings{Mghames2020, booktitle = { 2020 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS}) }, title = { Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking }, author = { Sariah Mghames and Marc Hanheide and Amir Ghalamzan E. }, publisher = { IEEE }, month = { oct }, year = { 2020 }, url = { https://doi.org/10.1109/iros45743.2020.9341728 }, doi = { 10.1109/iros45743.2020.9341728 }, }
2020	Conference	Soran Parsa et al. (2020). Haptic-guided shared control grasping: collision-free manipulation. In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). (link) (bib) x @inproceedings{Parsa2020, booktitle = { 2020 {IEEE} 16th International Conference on Automation Science and Engineering ({CASE}) }, title = { Haptic-guided shared control grasping: collision-free manipulation }, author = { Soran Parsa and Disha Kamale and Sariah Mghames and Kiyanoush Nazari and Tommaso Pardi and Aravinda R. Srinivasan and Gerhard Neumann and Marc Hanheide and Ghalamzan E. Amir }, publisher = { IEEE }, month = { aug }, year = { 2020 }, url = { https://doi.org/10.1109/case48305.2020.9216789 }, doi = { 10.1109/case48305.2020.9216789 }, }
2020	Conference	Nikos Mavrakis, Amir M. Ghalamzan E., Rustam Stolkin (2020). Estimating An Object's Inertial Parameters By Robotic Pushing: A Data-Driven Approach. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (link) (bib) x @inproceedings{Mavrakis2020, booktitle = { 2020 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS}) }, title = { Estimating An Object's Inertial Parameters By Robotic Pushing: A Data-Driven Approach }, author = { Nikos Mavrakis and Amir M. Ghalamzan E. and Rustam Stolkin }, publisher = { IEEE }, month = { oct }, year = { 2020 }, url = { https://doi.org/10.1109/iros45743.2020.9341112 }, doi = { 10.1109/iros45743.2020.9341112 }, }
2019	Conference	Mario Selvaggio, Amir M. Ghalamzan E, Rocco Moccia, Fanny Ficuciello, Bruno Siciliano (2019). Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (link) (bib) x @inproceedings{Selvaggio2019, booktitle = { 2019 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS}) }, title = { Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery }, author = { Mario Selvaggio and Amir M. Ghalamzan E and Rocco Moccia and Fanny Ficuciello and Bruno Siciliano }, publisher = { IEEE }, month = { nov }, year = { 2019 }, url = { https://doi.org/10.1109/iros40897.2019.8968109 }, doi = { 10.1109/iros40897.2019.8968109 }, }
2018	Journal	Amir M. Ghalamzan E., Matteo Ragaglia (2018). Robot learning from demonstrations: Emulation learning in environments with moving obstacles. Robotics and Autonomous Systems, 101, pp. 45–56 (link) (bib) x @article{GhalamzanE2018, journal = { Robotics and Autonomous Systems }, title = { Robot learning from demonstrations: Emulation learning in environments with moving obstacles }, author = { Amir M. Ghalamzan E. and Matteo Ragaglia }, pages = { 45--56 }, volume = { 101 }, publisher = { Elsevier {BV} }, month = { mar }, year = { 2018 }, url = { https://doi.org/10.1016/j.robot.2017.12.001 }, doi = { 10.1016/j.robot.2017.12.001 }, }
2017	Journal	Takayuki Osa, Amir M. Ghalamzan Esfahani, Rustam Stolkin, Rudolf Lioutikov, Jan Peters, Gerhard Neumann (2017). Guiding Trajectory Optimization by Demonstrated Distributions. IEEE Robotics and Automation Letters, 2(2), pp. 819–826 (link) (bib) x @article{Osa2017, journal = { {IEEE} Robotics and Automation Letters }, title = { Guiding Trajectory Optimization by Demonstrated Distributions }, author = { Takayuki Osa and Amir M. Ghalamzan Esfahani and Rustam Stolkin and Rudolf Lioutikov and Jan Peters and Gerhard Neumann }, pages = { 819--826 }, number = { 2 }, volume = { 2 }, publisher = { Institute of Electrical and Electronics Engineers ({IEEE}) }, month = { apr }, year = { 2017 }, url = { https://doi.org/10.1109/lra.2017.2653850 }, doi = { 10.1109/lra.2017.2653850 }, }
2017	Conference	M. Amir, E. Ghalamzan, Nikos Mavrakis, Rustam Stolkin (2017). Grasp that optimises objectives along post-grasp trajectories. In 2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM). (link) (bib) x @inproceedings{Amir2017, booktitle = { 2017 5th {RSI} International Conference on Robotics and Mechatronics ({ICRoM}) }, title = { Grasp that optimises objectives along post-grasp trajectories }, author = { M. Amir and E. Ghalamzan and Nikos Mavrakis and Rustam Stolkin }, publisher = { IEEE }, month = { oct }, year = { 2017 }, url = { https://doi.org/10.1109/icrom.2017.8466161 }, doi = { 10.1109/icrom.2017.8466161 }, }
2017	Conference	Nikos Mavrakis, E. Amir M. Ghalamzan, Rustam Stolkin (2017). Safe robotic grasping: Minimum impact-force grasp selection. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (link) (bib) x @inproceedings{Mavrakis2017, booktitle = { 2017 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS}) }, title = { Safe robotic grasping: Minimum impact-force grasp selection }, author = { Nikos Mavrakis and E. Amir M. Ghalamzan and Rustam Stolkin }, publisher = { IEEE }, month = { sep }, year = { 2017 }, url = { https://doi.org/10.1109/iros.2017.8206258 }, doi = { 10.1109/iros.2017.8206258 }, }
2017	Conference	E. Amir M. Ghalamzan, Firas Abi-Farraj, Paolo Robuffo Giordano, Rustam Stolkin (2017). Human-in-the-loop optimisation: Mixed initiative grasping for optimally facilitating post-grasp manipulative actions. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (link) (bib) x @inproceedings{Ghalamzan2017, booktitle = { 2017 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS}) }, title = { Human-in-the-loop optimisation: Mixed initiative grasping for optimally facilitating post-grasp manipulative actions }, author = { E. Amir M. Ghalamzan and Firas Abi-Farraj and Paolo Robuffo Giordano and Rustam Stolkin }, publisher = { IEEE }, month = { sep }, year = { 2017 }, url = { https://doi.org/10.1109/iros.2017.8206178 }, doi = { 10.1109/iros.2017.8206178 }, }
2016	Conference	M. Talha, E. A. M. Ghalamzan, C. Takahashi, J. Kuo, W. Ingamells, R. Stolkin (2016). Towards robotic decommissioning of legacy nuclear plant: Results of human-factors experiments with tele-robotic manipulation, and a discussion of challenges and approaches for decommissioning. In 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). (link) (bib) x @inproceedings{Talha2016, booktitle = { 2016 {IEEE} International Symposium on Safety, Security, and Rescue Robotics ({SSRR}) }, title = { Towards robotic decommissioning of legacy nuclear plant: Results of human-factors experiments with tele-robotic manipulation, and a discussion of challenges and approaches for decommissioning }, author = { M. Talha and E. A. M. Ghalamzan and C. Takahashi and J. Kuo and W. Ingamells and R. Stolkin }, publisher = { IEEE }, month = { oct }, year = { 2016 }, url = { https://doi.org/10.1109/ssrr.2016.7784294 }, doi = { 10.1109/ssrr.2016.7784294 }, }
2016	Conference	Amir M. Ghalamzan E., Nikos Mavrakis, Marek Kopicki, Rustam Stolkin, Ales Leonardis (2016). Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (link) (bib) x @inproceedings{GhalamzanE2016, booktitle = { 2016 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS}) }, title = { Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories }, author = { Amir M. Ghalamzan E. and Nikos Mavrakis and Marek Kopicki and Rustam Stolkin and Ales Leonardis }, publisher = { IEEE }, month = { oct }, year = { 2016 }, url = { https://doi.org/10.1109/iros.2016.7759158 }, doi = { 10.1109/iros.2016.7759158 }, }
2016	Conference	Nikos Mavrakis, Amir M. Ghalamzan E., Rustam Stolkin, Luca Baronti, Marek Kopicki, Marco Castellani (2016). Analysis of the inertia and dynamics of grasped objects, for choosing optimal grasps to enable torque-efficient post-grasp manipulations. In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids). (link) (bib) x @inproceedings{Mavrakis2016, booktitle = { 2016 {IEEE}-{RAS} 16th International Conference on Humanoid Robots (Humanoids) }, title = { Analysis of the inertia and dynamics of grasped objects, for choosing optimal grasps to enable torque-efficient post-grasp manipulations }, author = { Nikos Mavrakis and Amir M. Ghalamzan E. and Rustam Stolkin and Luca Baronti and Marek Kopicki and Marco Castellani }, publisher = { IEEE }, month = { nov }, year = { 2016 }, url = { https://doi.org/10.1109/humanoids.2016.7803274 }, doi = { 10.1109/humanoids.2016.7803274 }, }
2015	Conference	Amir M. Ghalamzan E, Chris Paxton, Gregory D. Hager, Luca Bascetta (2015). An incremental approach to learning generalizable robot tasks from human demonstration. In 2015 IEEE International Conference on Robotics and Automation (ICRA). (link) (bib) x @inproceedings{GhalamzanE2015, booktitle = { 2015 {IEEE} International Conference on Robotics and Automation ({ICRA}) }, title = { An incremental approach to learning generalizable robot tasks from human demonstration }, author = { Amir M. Ghalamzan E and Chris Paxton and Gregory D. Hager and Luca Bascetta }, publisher = { IEEE }, month = { may }, year = { 2015 }, url = { https://doi.org/10.1109/icra.2015.7139985 }, doi = { 10.1109/icra.2015.7139985 }, }
2015	Conference	Amir M. Ghalamzan E., Luca Bascetta, Marcello Restelli, Paolo Rocco (2015). Estimating a Mean-Path from a set of 2-D curves. In 2015 IEEE International Conference on Robotics and Automation (ICRA). (link) (bib) x @inproceedings{GhalamzanE2015, booktitle = { 2015 {IEEE} International Conference on Robotics and Automation ({ICRA}) }, title = { Estimating a Mean-Path from a set of 2-D curves }, author = { Amir M. Ghalamzan E. and Luca Bascetta and Marcello Restelli and Paolo Rocco }, publisher = { IEEE }, month = { may }, year = { 2015 }, url = { https://doi.org/10.1109/icra.2015.7139467 }, doi = { 10.1109/icra.2015.7139467 }, }

Showing 1 to 27 of 27 entries