10 papers · 1 filter
PEEL: Parallel Extraction for Long-Horizon Disassembly Planning via Scale-Invariant Sampling
Servet B. Bayraktar, Andreas Orthey, Zachary Kingston +1
Long-horizon multi-part object disassembly requires robots to compute feasible sequences of collision-free removal motions, even in the presence of tight, narrow escape corridors.…
Coordinated Multi-Robot Disassembly for Makespan Optimization of Large-Scale Assemblies
Niklas Hargus, Andreas Orthey, Marc Toussaint
Multi-robot task and motion planning for disassembly tasks requires robots to operate in confined workspaces while coordinating their motions with other robots. To tackle this prob…
PhyRoGen: Synthetic Generation of Physical Robot Manipulation Puzzles Using Procedural Content Generation
Lennart Julian Droß, Andreas Orthey, Marc Toussaint
Robot manipulation of physical puzzles is important for automatic assembly and disassembly tasks. However, to enable robots to solve physical puzzles, manipulation skills need to b…
Optimizing Trajectory-Trees in Belief Space: An Application from Model Predictive Control to Task and Motion Planning
Camille Phiquepal, Marc Toussaint
This paper explores the benefits of computing arborescent trajectories (trajectory-trees) instead of commonly used sequential trajectories for partially observable robotic planning…
Scale-Invariant Sampling in Multi-Arm Bandit Motion Planning for Object Extraction
Servet B. Bayraktar, Andreas Orthey, Marc Toussaint
Object extraction tasks often occur in disassembly problems, where bolts, screws, or pins have to be removed from tight, narrow spaces. In such problems, the distance to the enviro…
Stability-Guided Exploration for Diverse Motion Generation
Eckart Cobo-Briesewitz, Tilman Burghoff, Denis Shcherba +2
Scaling up datasets is highly effective in improving the performance of deep learning models, including in the field of robot learning. However, data collection still proves to be…