Search Results for author: Jon Scholz

Found 7 papers, 0 papers with code

RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation

no code implementations30 Aug 2023 Mel Vecerik, Carl Doersch, Yi Yang, Todor Davchev, Yusuf Aytar, Guangyao Zhou, Raia Hadsell, Lourdes Agapito, Jon Scholz

For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly.

Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation

no code implementations13 Apr 2023 Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar

We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model.

Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings

no code implementations9 Dec 2021 Mel Vecerik, Jackie Kay, Raia Hadsell, Lourdes Agapito, Jon Scholz

Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics.

Keypoint Detection Object +1

Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation

no code implementations ICLR 2022 Todor Davchev, Oleg Sushkov, Jean-Baptiste Regli, Stefan Schaal, Yusuf Aytar, Markus Wulfmeier, Jon Scholz

In this work, we extend hindsight relabelling mechanisms to guide exploration along task-specific distributions implied by a small set of successful demonstrations.

Continuous Control Reinforcement Learning (RL)

Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study

no code implementations21 Mar 2021 Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerik, Ning Ye, Stefan Schaal, Jon Scholz

In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark.

A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning

no code implementations2 Oct 2018 Mel Vecerik, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, Jon Scholz

Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing.


Cannot find the paper you are looking for? You can Submit a new open access paper.