233 citations · 528 across the 30 of their papers we have counts for
7 papers · 1 filter
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
Mel Vecerik, Carl Doersch, Yi Yang +6
For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly. Current approaches lack either the generality to onboard ne…
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao +36
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learnin…
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation
Todor Davchev, Oleg Sushkov, Jean-Baptiste Regli +4
Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of "narrow passages" in their state space. Solving such tasks with a spa…
Manipulator-Independent Representations for Visual Imitation
Yuxiang Zhou, Yusuf Aytar, Konstantinos Bousmalis
Imitation learning is an effective tool for robotic learning tasks where specifying a reinforcement learning (RL) reward is not feasible or where the exploration problem is particu…
Learning rich touch representations through cross-modal self-supervision
Martina Zambelli, Yusuf Aytar, Francesco Visin +2
The sense of touch is fundamental in several manipulation tasks, but rarely used in robot manipulation. In this work we tackle the problem of learning rich touch features from cros…
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
Rae Jeong, Yusuf Aytar, David Khosid +5
Collecting and automatically obtaining reward signals from real robotic visual data for the purposes of training reinforcement learning algorithms can be quite challenging and time…
Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov +13
We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show h…