Incremental Learning of Probabilistic Movement Primitives (ProMPs) for Human-Robot Cooperation
This work addresses the need for more intuitive and faster skill acquisition in human-robot cooperation, but it appears incremental as it builds on existing ProMP methods by adapting them to an incremental setting.
The paper tackles the problem of enabling robots to learn motor skills quickly from human demonstrations in physical human-robot cooperation by proposing an incremental learning algorithm for Probabilistic Movement Primitives, which allows sequential incorporation of demonstrations with a forgetting factor to adapt to changes, though no concrete performance numbers are provided in the abstract.
For a successful deployment of physical Human-Robot Cooperation (pHRC), humans need to be able to teach robots new motor skills quickly. Probabilistic movement primitives (ProMPs) are a promising method to encode a robot's motor skills learned from human demonstrations in pHRC settings. However, most algorithms to learn ProMPs from human demonstrations operate in batch mode, which is not ideal in pHRC. In this paper we propose a new learning algorithm to learn ProMPs incrementally in pHRC settings. Our algorithm incorporates new demonstrations sequentially as they arrive, allowing humans to observe the robot's learning progress and incrementally shape the robot's motor skill. A built in forgetting factor allows for corrective demonstrations resulting from the human's learning curve or changes in task constraints. We compare the performance of our algorithm to existing batch ProMP algorithms on reference data generated from a pick-and-place task at our lab. Furthermore, we show in a proof of concept study on a Franka Emika Panda how the forgetting factor allows us to adopt changes in the task. The incremental learning algorithm presented in this paper has the potential to lead to a more intuitive learning progress and to establish a successful cooperation between human and robot faster than training in batch mode.