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Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware
Learning instead of designing robot controllers can greatly reduce engineering effort required, while also emphasizing robustness. Despite considerable progress in simulation, applying learning directly in hardware is still challenging, in part due to the necessity to explore potentially unstable parameters. We explore the of concept shaping the reward landscape with training wheels; temporary modifications of the physical hardware that facilitate learning. We demonstrate the concept with a robot leg mounted on a boom learning to hop fast. This proof of concept embodies typical challenges such as instability and contact, while being simple enough to empirically map out and visualize the reward landscape. Based on our results we propose three criteria for designing effective training wheels for learning in robotics.
@inproceedings{shaping_icra2018, title = {Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018}, abstract = {Learning instead of designing robot controllers can greatly reduce engineering effort required, while also emphasizing robustness. Despite considerable progress in simulation, applying learning directly in hardware is still challenging, in part due to the necessity to explore potentially unstable parameters. We explore the of concept shaping the reward landscape with training wheels; temporary modifications of the physical hardware that facilitate learning. We demonstrate the concept with a robot leg mounted on a boom learning to hop fast. This proof of concept embodies typical challenges such as instability and contact, while being simple enough to empirically map out and visualize the reward landscape. Based on our results we propose three criteria for designing effective training wheels for learning in robotics.}, pages = {5076-5081}, publisher = {IEEE}, month = may, year = {2018}, slug = {robot-hopping-learning-hardware}, author = {Heim, Steve and Ruppert, Felix and Sarvestani, Alborz and Sproewitz, Alexander}, url = {https://arxiv.org/abs/1709.10273}, month_numeric = {5} }