About the Project
Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.
This project investigates intelligent robotic manipulation for deformable or soft objects by integrating visual and tactile sensing. Unlike rigid objects, deformable items (e.g., soft tissues, food products, or packaging materials) present significant challenges for robotic manipulation due to their unpredictable shapes and compliant properties. Relying on vision alone is often insufficient, as deformation and occlusion can distort perception. To address this, the project aims to develop a visual–tactile fusion framework that enables a robot to perceive, predict, and adapt its manipulating strategy in real time. The vision system provides global context—object geometry, pose, and deformation state—while tactile sensors supply local contact feedback such as pressure distribution, slip, and texture. By fusing these complementary modalities through deep learning or probabilistic models, the robot can achieve robust and adaptive control even under uncertain or variable conditions. The outcomes will advance the field of soft object manipulation and contribute to applications in healthcare, agriculture, and manufacturing.
The PhD will be based in the School of Electrical and Mechanical Engineering and will be supervised by Dr Xin Zhang.
The work on this project will:
- Perception of Deformable Objects. Combines RGB/depth vision with high-resolution tactile sensing to estimate deformation and contact states dynamically during manipulation.
- Multimodal Sensor Fusion. Develops a versatile fusion model using advanced machine learning algorithms (such as diffusion model, reinforcement learning) to integrate visual and tactile data, enhancing object recognition, contact estimation, and grasp stability prediction.
- Simulation training and experiment verification. The simulation will be used to train the algorithm. A large amount of deformed objects will be used to test the developed smart manipulation system
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