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Reinforcement learning (RL) can potentially solve complex problems in a purely data-driven manner. Still, the state-of-the-art in applying RL in robotics, relies heavily on high-fidelity simulators. While learning in simulation allows to circumvent sample complexity challenges that are common in model-free RL, even slight distribution shift ("sim-to-real gap") between simulation and the real system can cause these algorithms to easily fail. Recent advances in model-based reinforcement learning have led to superior sample efficiency, enabling online learning without a simulator. Nonetheless, learning online cannot cause any damage and should adhere to safety requirements (for obvious reasons). The proposed project aims to demonstrate how existing safe model-based RL methods can be used to solve the foregoing challenges. - Engineering and Technology
- Master Thesis
| Drying (e.g. Pasta drying) is the most energy intensive process step, sometimes taking up more than 50% of the total energy consumption of a plant. Superheated steam drying could present an energy efficient alternative to classical hot-air drying systems used today. This new technology could have a massive impact on the carbon-footprint and sustainability of food-drying; making it a highly future-oriented and potentially impactful innovation. - Interdisciplinary Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| The objective of this project is to create a comprehensive robotic platform capable of autonomously administering injections into the human eye. The project includes mechanical design, motion planning, and the implementation of a force control algorithm. - Mechanical and Industrial Engineering
- Bachelor Thesis, ETH Zurich (ETHZ), Master Thesis, Semester Project
| In this project, we propose a real-time sensorized hand-exoskeleton that combines the exosuit and the sensory feedback based on TENS systems. The integrated robotic system will be able to provide the user with assisted grasping force control via exo and with fine grip-force feedback. - Peripheral Nervous System, Rehabilitation Engineering, Therapies and Therapeutic Technology
- Master Thesis
| The project consists in assessing the degree to which our perception of natural texture is shaped by the mechanics of the skin. We have implemented an approach, developed by Ted Adelson at MIT (GelSight), that consists of fabricating a gel whose material properties match those of the skin and then imaging (using a laser profilometer) the pattern of deformation on the surface of the gel that is produced when pressed against the surface. We can then estimate how the skin would be deformed by any given texture using this approach and assess whether we can better predict from these patterns of skin deformation the responses of tactile nerve fibers to that texture and the perception thereof. - Biomedical Engineering, Central Nervous System, Sensory Systems
- Master Thesis
| Our aim is to create an autonomous racing system capable of swiftly learning optimal racing strategies and navigating tracks more effectively (faster) than traditional methods and human drivers using RL. - Intelligent Robotics
- Master Thesis, Semester Project
| Our previous studies has identified that the nature of signals recorded with electrodes implanted in the peripheral nervous system strongly depends on the type of electrode and the degree of activity inside the nerve. For this project your aim will be to further explore which dimension of a population activity a given type of electrode can identify. Based on the literature you will identify discharge patterns combination permitting to recreate the main different classes of bio-plausible population activity. You will also complete the existing hybrid model to integrate the main kinds of electrodes used with neural interfaces (use of Solidworks, Matlab, and Comsol).
You will then implement your bio-mimetic population activity in the full model and study each electrode’s dynamic selectivity. Your final goal would be to establish rules permitting to classify from the recording alone what “family” of activity is happening in the nerve.
- Biomedical Engineering, Electrical Engineering, Modeling and Simulation, Peripheral Nervous System
- Master Thesis
| This project aims at automatically learning problem-dependent uncertainty sets by exploiting available data on the uncertain parameters, hence surpassing the limitations of traditional methods such as robust and stochastic optimization approaches that assume the exact knowledge of the support set and of the probability distribution respectively. - Information, Computing and Communication Sciences, Optimisation, Systems Theory and Control
- Master Thesis, Semester Project
| Peripheral intraneural stimulation can provide tactile information to amputees. However, efforts are still necessary to identify encoding strategy eliciting percepts that are felt as both natural and effective for prosthesis control. Here we want to develop neural modulation strategies able to improve the naturalness and efficacy of stimulation to convey sensory information to trans-femoral amputees implanted with intraneural electrodes. - Electrical Engineering, Peripheral Nervous System, Rehabilitation Engineering, Sensory Systems
- Master Thesis
| Sensory feedback based on intracortical microstimulation has been shown to improve subjects’ ability to use brain-controlled bionic hands (Flesher et al., 2021). However, the resulting dexterity is still far from that of natural hands in able-bodied individuals. Efforts to sensitize bionic hands for amputees by electrical stimulation of the nerves have shown that sensory feedback that mimics natural tactile signals (so called biomimetic feedback (Okorokova et al., 2018; Saal and Bensmaia, 2015; Saal et al., 2017) evokes more natural and more intuitive sensations that better support interactions with objects than does non-biomimetic feedback. Despite these successes with amputees, biomimetic feedback has never been applied in the context brain-controlled bionic hands. - Biomedical Engineering, Central Nervous System, Electrical Engineering, Sensory Systems
- Master Thesis
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