Foundation Models in Explainable Robotics: Autonomously Extracting Robot-Internal Information for Explanations

Research topic/area

AI and Robotics

Type of thesis

Master

Start time

-

Application deadline

31.03.2026

Duration of the thesis

6 Monate

Description

Problem formulation
Intuitive Human-Robot Interaction requires robots to reason about their internal states and decision-making processes and to provide explanations for their acitons in a trustworthy and understandable way. Modern robots increasingly rely on learned models for perception and control, which often behave as black boxes, making it difficult to understand why decisions are made. At the same time, a variety of explainable AI techniques, as well as robot-internal information sources—such as sensor streams, logs, joint configurations, trajectories, and task histories, could potentially be used to provide insight into robot behavior. Foundation Models, with their language understanding and reasoning capabilities, offer a promising avenue for orchestrating such information and exploring how explanations can be generated in a flexible and adaptive manner.

Task definition
This thesis will implement a robotic manipulation task where the robot relies on
learned models to perform autonomous actions. The focus will be on investigating how internal robot states and learned components can be leveraged together with explainable AI methods to support explanation, and how Foundation Models might be applied to coordinate and synthesize this information. The effectiveness of this approach will be evaluated through experiments in which the robot executes the task and responds interactively to user questions with context dependent explanations.

Requirement

Requirements for students

• Solid knowledge base and experience in deep learning, and robotics.
• Coding skills in Python. Experience with Foundation Models, robot simulation and xAI is a plus.

Faculty departments

• Engineering sciences
  Electrical engineering & information technologies
  Informatics
  Mechanical engineering
  Mechatronics & information technologies
  Mechanical Engineering

Supervision

Title, first name, last name

Loris Schneider

Organizational unit

Institut für Fördertechnik und Logistiksysteme (IFL)

Email address

loris.schneider@kit.edu

Link to personal homepage/personal page

Website

Application via email

Application documents

• Curriculum vitae
• Grade transcript

E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an loris.schneider@kit.edu


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