Learning Multimodal Virtual Sensors for Adaptive Robotic Manipulation

All our international places for 2026 entry are now filled and we are no longer accepting applications from overseas students 
Research Theme

Autonomous Sensing Platforms

Sensor Signal Processing

Aim

Develop multimodal virtual sensing methods that leverage data-driven models to infer rich sensory feedback from minimal physical inputs, thereby enabling adaptive and reliable robotic manipulation in cost-constrained and hazardous environments. 

Objectives

  • Develop AI-based virtual sensor models that can approximate multimodal feedback (vision, haptics, force-torque, audio) from limited real sensor inputs.
  • Design and evaluate multimodal fusion strategies for learning robust object property and interaction representations that generalize across tasks and environments.
  • Investigate transfer learning and domain adaptation methods to enable deployment of virtual sensors trained on simulation or rich offline datasets to real-world robotic platforms.
  • Validate virtual sensing for manipulation tasks by benchmarking performance against fully instrumented systems in both controlled and hazardous/constrained scenarios.

Description

The PhD project investigates virtual sensing for robotic manipulation, focusing on the use of data-driven models to approximate multimodal sensory feedback. The core objective is to train AI models on rich sensory datasets (e.g., vision, haptics, force-torque, proprioception, audio) to learn robust representations of object properties and interaction dynamics. At deployment, these models will infer missing modalities from minimal physical sensing, enabling reliable manipulation in cost-constrained or hazardous environments. Key research challenges include multimodal fusion, domain adaptation, and the transfer of representations from simulation or offline data to real-world robotic systems. The project aims to advance theoretical understanding of virtual sensing architectures while delivering practical methods for adaptive, resource-efficient robotic manipulation.

Applications

First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.

Closing date: 
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Principal Supervisor

Eligibility

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Funding

Full funding is available for this position.