Publications

• Heiwolt, K., Mandil, W., Cielniak, G., & Hanheide, M. (2020). ‘Automated Topological Mapping for Agricultural Robots‘, UKRAS20 Conference: “Robots into the real world” Proceedings.
• Nazari, K., Mandil, W., Hanheide, M., & Ghalamzan Esfahani, A. (2021). ‘Tactile Dynamic Behaviour Prediction Based on Robot Action‘, Annual Conference Towards Autonomous Robotic Systems (TAROS 2021).
• Nazari, K., Mandil, W. & Ghalamzan Esfahani, A. (2022) Proactive slip control by learned slip model and trajectory adaptation. In: 6th Conference on Robot Learning, 14th-16th December 2022, Auckland, New Zealand.
• Mandil, W., Nazari, K., & Ghalamzan Esfahani, A. (2022) Action Conditioned Tactile Prediction: a case study on slip prediction. The Robotics: Science and Systems (RSS) 2022.
• Rajendran S.V., Mandil, W., Parsons, S., & Ghalamzan Esfahani, A. (2023) Acoustic Soft Tactile Skin (AST Skin). arXiv:2303.17355 [cs.RO].
• Mandil, W., & Ghalamzan Esfahani, A. (2023) Combining Vision and Tactile Sensation for Video Prediction. arXiv:2304.11193 [cs.RO].
• Rajendran S., Vishnu, D.B., Mghames, S., Mandil, W., Parsa, S., Parsons, S., & Ghalamzan Esfahani, A. (2023) Selective Harvesting Robots: A Review. Journal of Field Robotics . ISSN 1556-4959.
• Rajendran, V., Debnath, B., Mghames, S., Mandil, W., Parsa, S., Parsons, S., & Ghalamzan Esfahani, A. (2023) Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control. Journal of Field Robotics, Early View.
• Mandil, W., Rajendran, V., Nazari, K., & Ghalamzan Esfahani, A. (2023) Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation. Sensors, 23(17), pp. 7362.
• Nazari, K., Gandolfi, G., Talebpour, Z., Rajendran, V., Mandil, W., Rocco, P., & Ghalamzan, A.E. (2023) Deep Functional Predictive Control for Strawberry Cluster Manipulation using Tactile Prediction, Preprint.

Presentations

• UKRAS Conference (2020): Automated Topological Mapping for Agricultural Robots.
• Lincoln Conference on Intelligent Robots and Systems (2020): Investigation into Harvesting Soft Fruit Clusters.
• CRA Task-Informed Grasping Workshop – III (2021): Modelling soft fruit clusters for controlled harvesting. Watch here.
• AgriFoRwArdS CDT Annual Conference 2021, online (2021): Tactile prediction for controlled manipulation.
• Robotics: Science and Systems 2022: Action Conditioned Tactile Prediction: case study on slip prediction.

Posters

• 6th Conference on Robot Learning (CoRL 2022): Proactive slip control by learned slip model and trajectory adaptation.
• The Towards Autonomous Robots and Systems (TAROS) Conference 2023 / CDT Annual Conference / Joint Robotics CDT Conference (September 2023): Combining Visual and Tactile Sensation to Enhance Physical Robot Interactions.

Other Activities

• Took part in the AgriFoRwArdS Summer School 2021 resulting in co-authored presentation at the AgriFoRwArdS CDT Annual Conference 2021: Visual perception for harvesting grapes (in collaboration with Elijah Almanzor, Madeleine Darbyshire, Joshua Davy, Jerry Shi).
• Member of the discussion panel for Bob Fisher’s Keynote presentation at the AgriFoRwArdS CDT Annual Conference 2021, ‘The TrimBot2020 gardening robot and other agricultural robot issues’.
• Represented the CDT at the global climate change conference, COP26 in Glasgow (2021).
• Represented the CDT at the school outreach University of Lincoln British Science Week event (2022).
• Member of AgriFoRwArdS CDT Annual Conference 2022 discussion panel.
• Internship at the Toshiba Cambridge Research Laboratory, focusing on reinforcement learning, manipulation, and language models (February to March 2023).
• Internship at the Toshiba Cambridge Research Laboratory, focusing on novel Reinforcement Learning methods for robotic manipulation tasks (November 2023 to February 2024).

MSc Project

Investigation into Harvesting Soft Fruit Clusters

We propose an investigation into harvesting soft fruit clusters with robotics. Separating the problem into two distinct problems: motion planning to grasp the soft fruit (task 1); motion planning to release the soft fruit from the cluster (task 2). We will apply probabilistic movement primitives updated with model predictive control to task 1 and a probabilistic movement primitive architecture to task 2. We intend to test task 1 on strawberry clusters and task 2 on mushroom clusters.

PhD Project

Data-driven methods for detecting fruit and planning harvesting actions in dense cluster (DPFH)

Selective harvesting of soft fruit is a very challenging problem involving computer vision, motion planning, motion control, scheduling, and optimisation. State-of-the-art (SOTA) robotic system for selective harvesting of soft fruits are still far away from a human performance level, partially because the conventional planning cannot provide a feasible solution for the robot to reach-and-pick the soft fruit.   

DPFH aims to develop SOTA method for interactive motion planning and control for picking soft-fruits in a cluster where the robot needs to push the occluding pieces away to (i) better detect, segment and localise a ripe fruit and (ii) reach-and-pick the ripe fruit. The effectiveness of the developed methods in this project will be demonstrated in simulation environments, in the lab with toy strawberries and in the strawberry field.

Willow’s PhD project is being carried out in collaboration with Berry Garden’s, with primary supervision by Dr Amir Ghalamzan Esfahani.