6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics

no code implementations • 23 Mar 2023 • Maximilian Ulmer, Maximilian Durner, Martin Sundermeyer, Manuel Stoiber, Rudolph Triebel

We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model.

6D Pose Estimation using RGB

Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space

no code implementations • 19 Oct 2021 • Maximilian Ulmer, Elie Aljalbout, Sascha Schwarz, Sami Haddadin

We combine these components through a bio-inspired action space that we call AFORCE.

Decision Making reinforcement-learning +1

Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning

no code implementations • 2 Oct 2021 • Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel

Our method enhances the exploration capability of RL algorithms, by taking advantage of the SRL setup.

reinforcement-learning Reinforcement Learning (RL) +1

Making Curiosity Explicit in Vision-based RL

no code implementations • 28 Sep 2021 • Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel

Our method enhances the exploration capability of the RL algorithms by taking advantage of the SRL setup.

Reinforcement Learning (RL) Representation Learning

Learning Vision-based Reactive Policies for Obstacle Avoidance

no code implementations • 30 Oct 2020 • Elie Aljalbout, Ji Chen, Konstantin Ritt, Maximilian Ulmer, Sami Haddadin

In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators.