Solving Dexterous Manipulation Tasks with Trajectory Optimisation and Reinforcement Learning
Henry Charlesworth, Rowden Technologies
Bristol ML Meetup - 25/06/21
Motivation
- 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.
Reinforcement Learning
Gradient-free Trajectory Optimisation
Solving Dexterous Manipulation Tasks with RL and TrajOpt
By Henry Charlesworth
