100 citations · 186 across the 13 of their papers we have counts for
5 papers · 1 filter
Deep Visual Constraints: Neural Implicit Models for Manipulation Planning from Visual Input
Jung-Su Ha, Danny Driess, Marc Toussaint
Manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasping and placing an object, or mo…
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
Ingmar Schubert, Danny Driess, Ozgur S. Oguz +1
Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenario…
Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning
Danny Driess, Jung-Su Ha, Marc Toussaint +1
This work proposes an optimization-based manipulation planning framework where the objectives are learned functionals of signed-distance fields that represent objects in the scene.…
Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly
Valentin Noah Hartmann, Andreas Orthey, Danny Driess +2
Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the rob…
Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping
Marc Tuscher, Julian Hörz, Danny Driess +1
Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown en…