Student Thesis / Research Internship: Legged Robot Remote Inspection

19.05.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

Topic: Immersive Remote Inspection with a Legged Robot Using Learning-Based Stereo Vision

Motivation:
Legged robotic platforms (robot dogs) enable remote inspections in environments that may be unstructured or hazardous to humans. Their ability to navigate rough terrain makes them more versatile than wheeled alternatives. Equipping a highly mobile quadruped with a stereoscopic vision system and linking it to an operator's head-mounted display (HMD) creates a telepresence system that offers a natural first-person view of the remote site, while keeping the human operator safe and preserving their situational awareness.

One central challenge of this approach is latency. Any delay between the operator's head movement and the corresponding visual update on the HMD breaks the sense of presence and can lead to motion sickness. In practice, this can heavily limit uninterrupted teleoperation time. Addressing this requires intelligent compensation strategies that anticipate and mask transmission delays.


System:
The platform consists of a quadruped robot carrying a mechanically actuated stereoscopic camera system, which is wirelessly linked to the operator's HMD. The camera system mirrors the operator's head orientation in real time, providing a natural first-person perspective of the remote environment.


Research Project:
In this project, you will develop and evaluate latency compensation techniques for the stereoscopic vision system. By exploiting optical and geometric properties of the camera system, combined with learning-based networks that predict the operator's gaze and steering intent from sensor and control data, the system is supposed to proactively prepare visual outputs before they are needed. You will integrate your approach into the existing teleoperation platform and evaluate it through user studies. The work combines elements of computer vision, machine learning, robotics, communication engineering and human-robot interaction.


Goals:
Design and implement a latency compensation approach that exploits the vision system's design and develop learning-based intention prediction algorithms to maintain immersion under realistic network conditions. Ideally, you will be able to demonstrate a measurable reduction of motion sickness and visual discomfort compared to an uncompensated baseline. A central goal is to validate that operators can more comfortably perform continuous remote inspections for extended periods of time.


Requirements:
Interest in robotics, computer vision, and machine learning.

Good programming skills.

Excited to work with real hardware (robot platform, cameras, HMD).

Prior experience with the following is a plus: VR systems, ROS, stereo vision, deep learning frameworks.


If you are excited about the topic but don't check every box, feel free to reach out anyway!

Kontakt: valdrin.aslani@tum.de