Uploaded May 2018 | Updated September 2026, 3 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Wed PM Pod T.1
Authors: Heim, Steve Walter; Ruppert, Felix; Aghamaleki Sarvestani, Alborz; Sproewitz, Alexander
Title: Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware
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 concept of 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. A video synopsis can be found at youtu.be/6iH5E3LrYh8.
ICRA 2018 Spotlight Video
Interactive Session Wed PM Pod T.1
Authors: Heim, Steve Walter; Ruppert, Felix; Aghamaleki Sarvestani, Alborz; Sproewitz, Alexander
Title: Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware
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 concept of 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. A video synopsis can be found at youtu.be/6iH5E3LrYh8.
