10 papers
TaPeR: Probabilistic Recovery of Sparse Task Precedence Graphs from a Handful of Demonstrations
Adrian Röfer, Karla Stepanova, Abhinav Valada
Long-horizon manipulation tasks are often only partially ordered. For example, when assembling an electronic device, the battery and circuit board may be installed in either order,…
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning
Jan Ole von Hartz, Adrian Röfer, Joschka Boedecker +1
We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation. MiDiGap enables…
The Neural Compass: Probabilistic Relative Feature Fields for Robotic Search
Gabriele Somaschini, Adrian Röfer, Abhinav Valada
Object co-occurrences provide a key cue for finding objects successfully and efficiently in unfamiliar environments. Typically, one looks for cups in kitchens and views fridges as…
SparTa: Sparse Graphical Task Models from a Handful of Demonstrations
Adrian Röfer, Nick Heppert, Abhinav Valada
Learning long-horizon manipulation tasks efficiently is a central challenge in robot learning from demonstration. Unlike recent endeavors that focus on directly learning the task i…
Online Estimation and Manipulation of Articulated Objects
Russell Buchanan, Adrian Röfer, João Moura +2
From refrigerators to kitchen drawers, humans interact with articulated objects effortlessly every day while completing household chores. For automating these tasks, service robots…
Efficient Learning of Object Placement with Intra-Category Transfer
Adrian Röfer, Russell Buchanan, Max Argus +2
Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recen…