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Reinforcement Learning-Based Robot Design
Designing robots for different environments and tasks is a complex and time-consuming process that heavily relies on expert knowledge and human interventions. The automation and understanding of robot design, and the interplay between the structure and controller of a robot has long been a key research question. In recent years, reinforcement learning (RL) techniques have demonstrated great potential in solving complex graph generation problems across various fields, such as symbolic regression, AutoML, and chip design. Therefore, applying RL techniques to the robot design process based on the graph grammar[1] offers a promising solution to automating the design process and improving the robot's performance. Additionally, combining RL-based design with control optimization can lead to more efficient and effective robots with superior performance.
Keywords: Reinforcement Learning, Robotics, Optimization, Deep Learning
The primary goal of this project is to develop an RL-based solution based on graph grammar for automated robot design, and if possible, combine it with controller design or optimization. To achieve this, the project will involve the following tasks:
1. Conduct a comprehensive literature review on robot design optimization and control optimization.
2. Develop an RL-based framework for automated robot design and control optimization
3. Implement and evaluate the proposed method on a simulated robot platform[1].
4. Document the findings and contribute to research publications on the topic of RL and robotics
Requirements:
The candidate for this project should have:
1. A strong motivation to work on reinforcement learning and robotics
2. Proficiency in Python and familiarity with RL libraries (e.g., TensorFlow, PyTorch, OpenAI Gym).
3. Good communication skills and the ability to collaborate with others.
4. Currently enrolled as a student.
The primary goal of this project is to develop an RL-based solution based on graph grammar for automated robot design, and if possible, combine it with controller design or optimization. To achieve this, the project will involve the following tasks: 1. Conduct a comprehensive literature review on robot design optimization and control optimization. 2. Develop an RL-based framework for automated robot design and control optimization 3. Implement and evaluate the proposed method on a simulated robot platform[1]. 4. Document the findings and contribute to research publications on the topic of RL and robotics Requirements: The candidate for this project should have: 1. A strong motivation to work on reinforcement learning and robotics 2. Proficiency in Python and familiarity with RL libraries (e.g., TensorFlow, PyTorch, OpenAI Gym). 3. Good communication skills and the ability to collaborate with others. 4. Currently enrolled as a student.
The primary goal of this project is to develop an RL-based solution based on graph grammar for automated robot design, and if possible, combine it with controller design or optimization.
The primary goal of this project is to develop an RL-based solution based on graph grammar for automated robot design, and if possible, combine it with controller design or optimization.
Please send per email tian@ibi.baug.ethz.ch in a single PDF, a motivation letter (how is your profile relevant to the project and how is the project relevant for your career goals) and your transcripts.
Please send per email tian@ibi.baug.ethz.ch in a single PDF, a motivation letter (how is your profile relevant to the project and how is the project relevant for your career goals) and your transcripts.