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Lukas Klostermair

Junior Robotics Engineer specializing in tactile sensing and reinforcement learning

Stanford, USStudent Researcher (Thesis)3 years experienceJuniorRoboticsArtificial IntelligenceMachine Learning
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About

Robotics/ML engineer (Stanford project) who built a full Python-based RL grasping pipeline for an anthropomorphic tactile hand in MuJoCo, implementing SAC + behavioral cloning and proposing curriculum experiments; second author on an ICRCA 2026 submission. Hands-on with ROS 2 integration for Flexiv Rizon 4 and LEAP Hand, and uses Docker/Distrobox to manage complex CUDA/OS constraints while running training and production-style inference/retraining workflows.

Experience

Student Researcher (Thesis)Stanford Biomimetics & Dexterous Manipulation Lab (BDML)
Student ResearcherTechnical University of Munich Intelligent Neuroprostheses and Human Robotics (IN-HAND) Lab
ML Software EngineerBaind AG
Project MemberTechnical University of Munich & German Aerospace Center Learning AI for Dextrous Robots (AIDX) Lab
Student Researcher (Thesis)F. Hoffmann-La Roche AG
Technical Project Management (Working Student)Syskron GmbH / Krones AG
Innovation InternBMW AG

Education

Technical University of Munichmaster, Biomedical Engineering and Medical Physics (2026)
University of Applied Sciences Regensburgbachelor, Biomedical Engineering (2023)

Key Strengths

  • Built end-to-end robotics RL pipeline (MuJoCo + TensorFlow) including SAC implementation
  • Simulation fidelity work to match real robot behavior for calibration and debugging
  • Achieved zero-shot sim-to-real transfer for teleoperation calibration via recorded pose/motion validation
  • ROS 2 hardware integration across Flexiv Rizon 4 and LEAP Hand with multi-rate data handling (timestamps/buffering)
  • Strong containerization skills for robotics/ML workflows (Docker/Distrobox, CUDA compatibility)
  • Publication track record: second author on paper submitted to ICRCA 2026 (under review)

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Languages

EnglishGermanSpanish

Skills

RoboticsRobot LearningDexterous ManipulationRobotic ManipulationTactile SensingTactile/Force Sensor IntegrationReinforcement LearningSoft Actor-Critic (SAC)Proximal Policy Optimization (PPO)Twin Delayed DDPG (TD3)Imitation LearningDiffusion PoliciesSim-to-Real TransferDomain RandomizationCurriculum Learning