Search Results for author:
Found 8 papers, 6 papers with code
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
Our analysis, both in simulation and in the real world, shows that our approach is the best across data budgets, while standard offline RL from teacher rollouts is surprisingly effective when enough data is given.
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
We study the problem of robotic stacking with objects of complex geometry.
Ranked #2 on
Skill Generalization
on RGB-Stacking
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations.
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning
We demonstrate that this idea can be combined with a video-prediction based controller to enable complex behaviors to be learned from scratch using only raw visual inputs, including grasping, repositioning objects, and non-prehensile manipulation.
Stochastic Adversarial Video Prediction
However, learning to predict raw future observations, such as frames in a video, is exceedingly challenging -- the ambiguous nature of the problem can cause a naively designed model to average together possible futures into a single, blurry prediction.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Self-Supervised Learning of Object Motion Through Adversarial Video Prediction
In this paper, we study the problem of multi-step video prediction, where the goal is to predict a sequence of future frames conditioned on a short context.
Self-Supervised Visual Planning with Temporal Skip Connections
One learning signal that is always available for autonomously collected data is prediction: if a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location.
Learning Visual Servoing with Deep Features and Fitted Q-Iteration
Our approach is based on servoing the camera in the space of learned visual features, rather than image pixels or manually-designed keypoints.
