Todor Davchev, Daniel Gordon
Thu 05 Dec 2019, 12:45 - 14:00
IF, G.03

If you have a question about this talk, please contact: Jodie Cameron (jcamero9)

Todor Davchev

Title: Robust Skill Acquisition with Residual Policy Learning 

Abstract: Dealing with contacts and friction is a common requirement for modern manufacturing. However, relying on a control engineer to manually tune every deployed conventional feedback controller is often expensive and limited to a specific task. Reinforcement learning (RL) methods, on the other hand, have been shown to successfully learn continuous robot controllers for physical skill acquisition tasks on the often expensive cost of hyper-parameter tuning and data collection. In this work, we propose a framework that can acquire the skill of insertion in the context of a series of nonlinear physical tasks solved by a robot arm. We formulate our solution by combining behavioural cloning with a residual policy component solved with RL. In this work we show we complete a task in a quick and efficient manner. The final learned policy is then defined as a combination of both control signals.




Daniel Gordon

Title: Human-in-the-loop Optimisation of Exoskeleton Assistance Patterns

Abstract: Over the past few decades, many assistive robotic devices have been developed. Despite advancements in design and control algorithms, the problem of assisting locomotion remains challenging. Human walking strategies are unique and complex, and assistance strategies based on the dynamics of unassisted locomotion typically offer only modest reductions to the metabolic cost of walking. Recently, human-in-the-loop (HIL) methodologies have been used to identify assistive strategies which offer significant improvements to energy savings. However, current implementations suffer from long measurement times, necessitating the use of low-dimensional control parameterisations, and possibly requiring multi-day collection protocols to avoid subject fatigue. We present a HIL methodology which optimises the assistive torques provided by a powered hip exoskeleton. Using musculoskeletal modelling, we are able to evaluate simulated metabolic rate online, which reduces measurement times compared to previous methods. Our framework could be used to enable shorter HIL protocols or explore more complex control parameterisations.