Open Student Thesis Offers

Adaptive Reachability Analysis to Safe Driving of Autonomous Vehicles

[MA] Adaptive Reachability Analysis to Safe Driving of Autonomous Vehicles 

Reinforcement Learning, Representation Learning, Meta-RL and Robotics

Three of our DEMOs can be found at https://sites.google.com/view/kuka-environment/ , https://sites.google.com/view/cemrl, and https://videoviewsite.wixsite.com/rlsnake.

1. Meta-RL:
   • Context-based Meta-Reinforcement Learning with Bayesian Nonparametric Models
   • Exploiting DATA Symmetries in Context-Based Meta-Reinforcement Learning
   • [BA/MA] Meta Reinforcement Learning in Dynamic Environment 
2. RL:
   • [BA/MA] Complex Robotic Manipulation via Actionable Representation Learning Guided Exploration 
   • [BA/MA] Reinforcement Learning via Hindsight Experience Replay (HER)
   • [BA/MA] Reinforcement Learning for Adaptive Locomotion of Snake-like Robot
   • [BA/MA] Energy-Efficient Gait Exploration for Snake-like Robots Based on Adversarial Reinforcement Learning
   • [BA/MA] Imitation Learning via Demonstration
   • [BA/MA] Reinforcement Learning via Hindsight Goal Generation (HGG)
3. Language-conditioned Meta-RL: 
   • ​​​​[BA/MA] Language-conditioned Meta-reinforcement Learning for Multi-manipulation tasks
4. Safety Control for Robotic Manipulator
   • [BA/MA] Safe Robotic Manipulation Control with TENG Sensor and Control Barrier function Based on Machine Learning

We have several other open topics in the domain of reinforcement learning in robotics and we also accept open proposals or ideas with yourselves. For more information, please contact Zhenshan Bing.

Neural SLAM and Biomimetic Rodent Robot

• [MA/BA] Design and Control of a Rat Robot with Actuated Spine and Ribs
• [MA/BA] Biologically Plausible Spatial Navigation (NeuralSLAM)
• [MA/BA] Brain-inspired Localization and Mapping based on LiDAR Sensor

For more information about this topic, please contact Zhenshan Bing and Florian Walter.

KI.FABRIK: Future AI & Robotic Factory

1. Path Planning:

• Path Planning Algorithms via Open Motion Planning Library (OMPL)
• DARKO - Mobile Robot Manipulator Simulation and Trajectory Planning
• Visualization of the mobile robot manipulator for the DARKO project

 2. Obstacle Avoidance:

• Algorithms for Multiple Dynamic/Static Obstacle Avoidance

 3. Dynamics Learning:

• Data-Driven Identification and Model Reduction of Dynamic Model

AUTOtech.agil

• [MA/GR/BA] Visual Language Model in Autonomous Driving
• [MA/GR/BA] Domain Adaptation of Synthetic Data for 3D Traffic Environment Perception 
• Real-Time and Robust 3D Object Detection on the Autonomous Driving Test Stretch Using LiDAR Point Cloud Data
• Real-Time and Multi-Modal 3D Object Detection on the Autonomous Driving Test Stretch Using Camera and LiDAR Sensors
• Deep Traffic Scenario Mining, Detection, Classification and Generation on the Autonomous Driving Test Stretch using the CARLA Simulator
• Real-Time Camera-Only 3D Object Detection for Autonomous Driving
• Automated Camera Stabilization and Calibration for Intelligent Transportation Systems

Systems and Software Engineering

Our group provides a range of topics related to systems and software engineering with applications in robotics and automotive.

Currently, the following thesis proposals are open:

• [BA/MA] Containerizing ROS: Efficient Deployment and Management of Robotic Applications
• [BA/MA] Performance study on transporting large-scale dataset.
• [MA] Automated Design Space Exploration for Automotive Resource Allocation
• [MA] Exploring in-vehicle TIme-Sensitive Network scheduling based on formal requirements 
• [MA] Multi-robot cooperation under signal temporal logic
• [MA] From Natural language to Formal Automotive Architectural Requirements and Vice Versa

Federated Learning, Autonomous Vehicle Simulations, Reinforcement Learning

There are a few simulation oriented Bachelor and Master thesis in the following topics. If you are interested, feel free to contact one of us at nagacharan.tangirala@tum.de, erdi.sayar@tum.de

1. Evaluation of Federated Learning Techniques

2. Reinforcement Learning for robotics

3. Autonomous Vehicle Fleet Management

4. V2X Simulations with Edge Computing

 

Learning and Intuitive Robot Programming

Our group provides a range of topics related to human-robot interaction, intuitive robot programming, motion and grasp planning, deep learning, etc. 

Currently, the following thesis proposals are open:

• [BA/MA] SemanticPointCLIP: Vision-Language for Point Cloud Semantic Understanding
• [BA/MA] GraspDETR: DETR based Grasp Planning

Simulation-Based Learning Control for Real-World Robotic Manipulation and Navigation

• [BA/MA/GR/IDP] Comparative Sim2Real Path Planning for AGV Navigation

For more information about other available topics in reinforcement learning, data-driven control and sim2real transfer, please contact Hossein Malmir.

DeepSLAM: Deep Learning based Localization and Mapping (Vision-based Perception and Navigation)

• [MA/BA/GR] DeepSLAM: Deep Learning based Localization and Mapping
• [MA/BA/GR] Simultaneous Localization and Mapping in Dynamic Environments

Autonomous Vehicles

The Cyber Physical Systems group is pursuing a wide range of research directions related to safe decision making, motion planning and control for autonomous vehicles, involving both formal methods, sampling- and optimization-based methods as well as deep learning-based methods.

Currently, the following thesis proposals are open:

• [GR/MA] Learning Model Predictive Robustness of Probabilistic Signal Temporal Logic
• [BA/MA] Encoding the Future: Deep Representations for Traffic using Graph Neural Networks
• [BA/MA] Learning Isometric Embeddings of Road Networks using Multidimensional Scaling
• [MA] Deep Multi-Step Planning for Autonomous Driving
• [MA] Graph Neural Networks for Deep Behavior Prediction in Traffic Scenes
• [BA/MA] Model-Based Reinforcement Learning in Shadow Mode

Spiking Neural Networks - Next Generation AI for Autonomous Driving

At the KI-ASIC project we are researching about the application of bio-inspired neural networks to real-world applications.

If you are interested in learning about neuroscience and how neuromorphic engineering is trying to narrow the gap between biology an technology, do not hesitate to contact us.

Available:

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Previous topics:

• [BA|MA|GR] Efficient radar collision detection on neuromorphic hardware

Safe Reinforcement Learning in Robotics

Currently open positions:

• [MA] Ensuring Human Safety for AI-based Robot Control in ROS 2

Additionally, we will have open topics in safe reinforcement learning for manipulators and mobile platforms in the future.
If you are interested in these topics, you can contact: jakob (dot) thumm (at) tum.de

Modular Robotics

For interest in a BA/MA thesis in machine learning for modular robotics, please contact Jonathan Kuelz or Matthias Mayer.

Currently open:

• [MA]: Hybrid Reinforcement Learning for Robot Design: Description
• [MA/SA/BA] Efficient Path Planning for Modular Robots
• [MA/SA/BA] Solving Real-world Robotics Tasks within 3D Scans

Robust and Nonlinear Motion Planning & Control

• [MA] Safe and Efficient Inspection of Power Lines using UAVs
• [MA] Robust Control of Linear Systems with Uncertain Parameters
• [MA] Safety Certification for Learning-Based Control

Deep Learning for Computer Vision

If you are interested in researching trending topics in the field of computer vision and pattern recognition:

• Object detection
• Segmentation
• Tracking
• Surface reconstruction
• Mesh generation
• Text-to-image generation
• Domain adaptation of synthetic data

then please contact: Bare Luka Žagar

Formal Methods and Reachability Analysis

• [BA or MA] Data-driven Identification of Uncertainty Sets for Autonomous Systems
• [BA or MA] Uniform Trajectory Planning for Cyber-Physical Systems
• [BA or MA] Optimization-based Verification of Cyber-Physical Systems
• [MA] Ensuring Safety of Large-Scale Structures
• [MA] Exploiting Mixed-Monotonicity in Reachability Analysis
• [MA] Errors of Trajectories for Autonomous Vehicles and Cyber-Physical Systems

Offline Reinforcement Learning

[Research internship] Mastering the game of Skat using decision transformers

Safe Reinforcement Learning, Multi-Agent Reinforcement Learning

• [BA/MA]: Provably Safe Reinforcement Learning Control of a Quadrotor
• [BA/MA]: Safe Reinforcement Learning in Shadow Mode

Neurorobotics in the Human Brain Project

• Developmental Body Modeling in Soft Robotics
• Cloud-Based Robotics for Machine Learning
• Virtual Neurorobotics with Intel Loihi
• Spiking Compliant Robot Control with Intel Loihi
• Integration of the Neural Simulator NEST into the Neurorobotics Platform
• Deep Spiking Q-Networks
• Autonomous Locomotion Control for Snake Robot Based on Bio inspired Vision Sensor and Spiking Neural Network
• Advanced Autonomous Driving Control Based on Bio inspired Vision Sensor and Spiking Neural Network
• Spiking Neural Network for Autonomous Navigation based on LiDAR Sensor
• Deep Spiking Reinforcement Learning
• Learning adaptive target reaching with Recurrent Neural Networks
• Biologically-inspired Perception for Autonomous Vehicles based on LiDAR Sensor

SPEED-CARGO Project

• Knowledge Base and Inference Models for AI-based Optimiza-tion and Control of Air Cargo ProcessesMathematical Programming Models Processes Availability
• Deep learning applied to Real World Robotic System
• Mathematical Programming Models and Optimization for Air Cargo Processes

Machine Learning Algorithms for Hybrid Vehicle Data

Please see this page for the available topics about 3D Object Detection and Tracking.

Project SunRISE: Anomaly Detection in Time-Sensitive Networks

• [MA] Anomaly Detection in 1588-PTP Synchronized Clocks

Autonomous Robot & Visual Servo & Deep Learning & Robot Design & Medical Robotics

For more information, please visit my homepage Mingchuan Zhou or contact me via email (zhoum@in.tum.de).

Data Analysis and Deep Learning in Robot-Based Additive Manufacturing

BA / IDP / MA - partly in cooperation with Siemens AG

For more information, please visit my homepage Raven Reisch or contact me via email (raven.reisch@tum.de).

OSBORNE (Future Automotive E/E Architectures for Autonomous Cars)

We have a set of open topics in the domain of affective computing and multimodal emotion recognition, within the context of OSBORNE project, for more information please contact Sina.

HORSE / REMODEL

We have several open topics related to computer vision in the HORSE and REMODEL projects. Please contact Arne for more details.

Collaboration with Chair for Product Development and Lightweight Design

• Design and Control of a Humanoid Robot Arm

External thesis proposals

• Masterthesis – Grey-Box Modellierung und Validierung des Motoraufheizverhaltens auf dem Rollenprüfstand und im Fahrzeug mittels Machine Learning
• [MA] Sim2real robotic grasping of unknown objects with multi-fingered hand (Agile Robots AG)