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GSRM: Building Roadmaps for Query-Efficient and Near-Optimal Path Planning Using a Reaction Diffusion System
Christian Henkel, Marc Toussaint, Wolfgang Hönig
Mobile robots frequently navigate on roadmaps, i.e., graphs where edges represent safe motions, in applications such as healthcare, hospitality, and warehouse automation. Often the…
NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Sampling
Marc Toussaint, Cornelius V. Braun, Joaquim Ortiz-Haro
Generating diverse samples under hard constraints is a core challenge in many areas. With this work we aim to provide an integrative view and framework to combine methods from the…
Solving Sequential Manipulation Puzzles by Finding Easier Subproblems
Svetlana Levit, Joaquim Ortiz-Haro, Marc Toussaint
We consider a set of challenging sequential manipulation puzzles, where an agent has to interact with multiple movable objects and navigate narrow passages. Such settings are notor…
iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization
Joaquim Ortiz-Haro, Wolfgang Hönig, Valentin N. Hartmann +2
Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic con…
Global Safe Sequential Learning via Efficient Knowledge Transfer
Cen-You Li, Olaf Duennbier, Marc Toussaint +2
Sequential learning methods, such as active learning and Bayesian optimization, aim to select the most informative data for task learning. In many applications, however, data selec…