Solving Dexterous Manipulation Tasks with Trajectory Optimisation and Reinforcement Learning
Henry Charlesworth, Rowden Technologies
Bristol ML Meetup - 25/06/21
Most robots in industry use parallel jaw grippers to manipulate objects.
Developing autonomous robots that can perform a wider variety of tasks in unstructured/uncertain environments will require more sophisticated manipulators.
The human hand is probably the most versatile and sophisticated manipulator we know of.
Natural to try and create robotic hands based on a human hand - and to try and train those robotic hands to perform complex manipulation tasks!
Why this is Hard?
Complex, discontinuous contact patterns between the hand and object make it difficult to come up with an accurate Physics model to optimise.
High-dimensional inputs/actions, and generally high precision and coordination between many joints is required to perform tasks well.
Traditional robotic control approaches struggle with these kinds of problem.
Gradient-free Trajectory Optimisation