Search Results for author: Jonathan Scholz

Found 7 papers, 2 papers with code

S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency

no code implementations30 Sep 2020 Mel Vecerik, Jean-Baptiste Regli, Oleg Sushkov, David Barker, Rugile Pevceviciute, Thomas Rothörl, Christopher Schuster, Raia Hadsell, Lourdes Agapito, Jonathan Scholz

In this work we advocate semantic 3D keypoints as a visual representation, and present a semi-supervised training objective that can allow instance or category-level keypoints to be trained to 1-5 millimeter-accuracy with minimal supervision.

Image Reconstruction Representation Learning

Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient

no code implementations15 Nov 2019 Kevin Sebastian Luck, Mel Vecerik, Simon Stepputtis, Heni Ben Amor, Jonathan Scholz

This work evaluates the use of model-based trajectory optimization methods used for exploration in Deep Deterministic Policy Gradient when trained on a latent image embedding.

Continuous Control reinforcement-learning

Generative predecessor models for sample-efficient imitation learning

no code implementations ICLR 2019 Yannick Schroecker, Mel Vecerik, Jonathan Scholz

We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observed in expert demonstrations, using generative models to reason probabilistically about alternative histories of demonstrated states.

Imitation Learning

PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations

no code implementations27 May 2017 Rico Jonschkowski, Roland Hafner, Jonathan Scholz, Martin Riedmiller

We propose position-velocity encoders (PVEs) which learn---without supervision---to encode images to positions and velocities of task-relevant objects.

Image Reconstruction

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