Search Results for author:
Found 16 papers, 9 papers with code
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
To our knowledge, PTR is the first RL method that succeeds at learning new tasks in a new domain on a real WidowX robot with as few as 10 task demonstrations, by effectively leveraging an existing dataset of diverse multi-task robot data collected in a variety of toy kitchens.
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning.
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
Robot learning holds the promise of learning policies that generalize broadly.
Model-Based Visual Planning with Self-Supervised Functional Distances
In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot.
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
In this work we propose a framework for visual prediction and planning that is able to overcome both of these limitations.
OmniTact: A Multi-Directional High Resolution Touch Sensor
We compare with a state-of-the-art tactile sensor that is only sensitive on one side, as well as a state-of-the-art multi-directional tactile sensor, and find that OmniTact's combination of high-resolution and multi-directional sensing is crucial for reliably inserting the electrical connector and allows for higher accuracy in the state estimation task.
RoboNet: Large-Scale Multi-Robot Learning
This leads to a frequent tension in robotic learning: how can we learn generalizable robotic controllers without having to collect impractically large amounts of data for each separate experiment?
Goal-Conditioned Video Prediction
Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video.
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight
Machine learning techniques have enabled robots to learn narrow, yet complex tasks and also perform broad, yet simple skills with a wide variety of objects.
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging.
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains.
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.
Time-Agnostic Prediction: Predicting Predictable Video Frames
Prediction is arguably one of the most basic functions of an intelligent system.
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.
