Learning Singularity Avoidance With the increase in complexity of robotic systems and the rise in nonexpert users, it can be assumed that task constraints are not explicitly known. In tasks where avoiding singularity is critical to success, this paper provides an approach, especially for nonexpert 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 them. 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 systemxe2x80x99s manipulability subject to a taskxe2x80x99s constraints, the proposed approach can be used instead to infer these with results showing errors less than 105 in 3DOF simulated systems and less than 102 using a 7DOF real world robotic system.

Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning In this paper, we propose a reinforcement learningbased algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for most robotic systems, the dynamics may not always be known. Generating smooth, dynamically feasible trajectories could be difficult for such systems. Using samplingbased algorithms for motion planning may result in trajectories that are prone to undesirable control jumps. However, they can usually provide a good reference trajectory which a modelfree reinforcement learning algorithm can then exploit by limiting the search domain and quickly finding a dynamically smooth trajectory. We use this idea to train a reinforcement learning agent to learn a dynamically smooth trajectory in a curriculum learning setting. Furthermore, for generalization, we parameterize the policies with goal locations, so that the agent can be trained for multiple goals simultaneously. We show result in both simulated environments as well as real experiments, for a 6DoF manipulator arm operated in positioncontrolled mode to validate the proposed idea. We compare the proposed ideas against a PID controller which is used to track a designed trajectory in configuration space. Our experiments show that our RL agent trained with a reference path outperformed a modelfree PID controller of the type commonly used on many robotic platforms for trajectory tracking.