Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates

Neumann K, Lemme A, Steil JJ. Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates. Presented at the Int. Conference Intelligent Robotics and Systems, Tokio

OOP: Object-Oriented-Priority for Motion Saliency Maps

Belardinelli A, Schneider WX, Steil JJ. OOP: Object-Oriented-Priority for Motion Saliency Maps. In: Workshop on Brain Inspired Cognitive Systems. 2010: 370-381

Platform Portable Anthropomorphic Grasping with the Bielefeld 20-DOF Shadow and 9-DOF TUM Hand

Röthling F, Haschke R, Steil JJ, Ritter H. Platform Portable Anthropomorphic Grasping with the Bielefeld 20-DOF Shadow and 9-DOF TUM Hand. In: Proc. Int. Conf. on Intelligent Robots and Systems (IROS). IEEE; 2007: 2951-2956

Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot

Reinhart F, Steil JJ. Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot. In: Procedia Technology. Vol 26. 2016: 12-19

Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks

Aswolinskiy W, Steil JJ. Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks. In: Proceedings of the Workshop on New Challenges in Neural Computation. Machine Learning Reports. 2016

Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces

Queißer J, Steil JJ. Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces. Frontiers in Robotics and AI. 2018;5:49.Modern robotic applications create high demands on adaptation of actions with respect to variance in a given task. Reinforcement learning is able to optimize for these changing conditions, but relearning from scratch is hardly feasible due to the high number of required rollouts. We propose a parameterized skill that generalizes to new actions for changing task parameters, which is encoded as a meta-learner that provides parameters for task-specific dynamic motion primitives. Our work shows that utilizing parameterized skills for initialization of the optimization process leads to a more effective incremental task learning. In addition, we introduce a hybrid optimization method that combines a fast coarse optimization on a manifold of policy parameters with a fine grained parameter search in the unrestricted space of actions. The proposed algorithm reduces the number of required rollouts for adaptation to new task conditions. Application in illustrative toy scenarios, for a 10-DOF planar arm, and a humanoid robot point reaching task validate the approach

Indices to Evaluate Self-Organizing Maps for Structures

Self-Organizing Maps for Structures (SOM-SD) are neural networks models capable of processing structured data, such as sequences and trees. The evaluation of the encoding quality achieved by these maps should neither be measured only by the quantization error as in the standard SOM, which fails to capture the structural aspects, nor by other topology preserving indexes which are ill-defined for discrete structures. We propose new indexes for the evaluation of encoding quality which are customized to the structural nature of input data. These indexes are used to evaluate the quality of SOM-SDs trained on a benchmark dataset introduced earlier in. We show that the proposed indexes capture relevant structural features of the tree encoding additional to the statistical features of the training data labels

Goal Babbling with direction sampling for simultaneous exploration and learning of inverse kinematics of a humanoid robot

Rayyes R, Steil JJ. Goal Babbling with direction sampling for simultaneous exploration and learning of inverse kinematics of a humanoid robot. In: Proceedings of the workshop on New Challenges in Neural Computation. Machine Learning Reports. Vol 4. 2016: 56-63