Search Results for author: Oliver Brock

Found 8 papers, 2 papers with code

"The World Is Its Own Best Model": Robust Real-World Manipulation Through Online Behavior Selection

no code implementations9 May 2022 Manuel Baum, Oliver Brock

Robotic manipulation behavior should be robust to disturbances that violate high-level task-structure.

The RBO Dataset of Articulated Objects and Interactions

no code implementations17 Jun 2018 Roberto Martín-Martín, Clemens Eppner, Oliver Brock

Each interaction with an object is annotated with the ground truth poses of its rigid parts and the kinematic state obtained by a motion capture system.

Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors

3 code implementations28 May 2018 Rico Jonschkowski, Divyam Rastogi, Oliver Brock

We present differentiable particle filters (DPFs): a differentiable implementation of the particle filter algorithm with learnable motion and measurement models.

The Limits and Potentials of Deep Learning for Robotics

no code implementations18 Apr 2018 Niko Sünderhauf, Oliver Brock, Walter Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, Peter Corke

In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning.

Robotics

Interactive Perception: Leveraging Action in Perception and Perception in Action

no code implementations13 Apr 2016 Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, Danica Kragic, Stefan Schaal, Gaurav Sukhatme

Recent approaches in robotics follow the insight that perception is facilitated by interaction with the environment.

Robotics

Analysis and Observations from the First Amazon Picking Challenge

no code implementations21 Jan 2016 Nikolaus Correll, Kostas E. Bekris, Dmitry Berenson, Oliver Brock, Albert Causo, Kris Hauser, Kei Okada, Alberto Rodriguez, Joseph M. Romano, Peter R. Wurman

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.

Robotics

Patterns for Learning with Side Information

1 code implementation19 Nov 2015 Rico Jonschkowski, Sebastian Höfer, Oliver Brock

Supervised, semi-supervised, and unsupervised learning estimate a function given input/output samples.

Multi-Task Learning MULTI-VIEW LEARNING

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