Learning Singularity Avoidance

With the increase in complexity of robotic systems and the rise in non-expert users, it can be assumed that task constraints are not explicitly known. In tasks where avoiding singularity is critical to its success, this paper provides an approach, especially for non-expert users, for the system to learn the constraints contained in a set of demonstrations, such that they can be used to optimise an autonomous controller to avoid singularity, without having to explicitly know the task constraints. The proposed approach avoids singularity, and thereby unpredictable behaviour when carrying out a task, by maximising the learnt manipulability throughout the motion of the constrained system, and is not limited to kinematic systems. Its benefits are demonstrated through comparisons with other control policies which show that the constrained manipulability of a system learnt through demonstration can be used to avoid singularities in cases where these other policies would fail. In the absence of the systems manipulability subject to a tasks constraints, the proposed approach can be used instead to infer these with results showing errors less than 10^-5 in 3DOF simulated systems as well as 10^-2 using a 7DOF real world robotic system

Learning Singularity Avoidance - Data using a real world 7 link sawyer robot and simulated 3 link planar system

This dataset contains both real world and simulated data. The real world data consists of 50 trajectories of 7DOF Jointspace data recorded kinaesthetically using the Sawyer robot. Each demonstration has 1) a task space component where the sawyer moves from a starting position to anywhere on a drawer and 2) a null space component where the system is closing the drawer. The constraint in the null space limits movement of the system to the x-axis. These can be read as text files. The simulated data consists of 3 sets of 50 trajectories of 3DOF Jointspace data in a planar system. In each set the system is constrained by one of the 3 constraints: 1) X and Y, 2) X and Theta and 3) Y and Theta. These are stored as matlab data.For further details please refer to the paper: Learning Singularity Avoidance Manavalan, J. & Howard, M. J. W., 2019, IEEE/RSJ International Conference on Intelligent Robots and Systems