most citedSpeeding Up Optimization-based Motion Planning through Deep Learning

14 citations · 36 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2023★ 5 cited

Self-Contained and Automatic Calibration of a Multi-Fingered Hand Using Only Pairwise Contact Measurements

Johannes Tenhumberg, Leon Sievers, Berthold Bäuml

A self-contained calibration procedure that can be performed automatically without additional external sensors or tools is a significant advantage, especially for complex robotic s…

cs.RO2023★ 14 cited

Speeding Up Optimization-based Motion Planning through Deep Learning

Johannes Tenhumberg, Darius Burschka, Berthold Bäuml

Planning collision-free motions for robots with many degrees of freedom is challenging in environments with complex obstacle geometries. Recent work introduced the idea of speeding…

cs.RO2023★ 7 cited

Self-Contained Calibration of an Elastic Humanoid Upper Body Using Only a Head-Mounted RGB Camera

Johannes Tenhumberg, Dominik Winkelbauer, Darius Burschka +1

When a humanoid robot performs a manipulation task, it first makes a model of the world using its visual sensors and then plans the motion of its body in this model. For this, prec…

cs.RO2023★ 7 cited

Calibration of an Elastic Humanoid Upper Body and Efficient Compensation for Motion Planning

Johannes Tenhumberg, Berthold Bäuml

High absolute accuracy is an essential prerequisite for a humanoid robot to autonomously and robustly perform manipulation tasks while avoiding obstacles. We present for the first…

cs.RO2023★ 3 cited

Efficient Learning of Fast Inverse Kinematics with Collision Avoidance

Johannes Tenhumberg, Arman Mielke, Berthold Bäuml

Fast inverse kinematics (IK) is a central component in robotic motion planning. For complex robots, IK methods are often based on root search and non-linear optimization algorithms…