20,192 research outputs found
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
We propose a technique for multi-task learning from demonstration that trains
the controller of a low-cost robotic arm to accomplish several complex picking
and placing tasks, as well as non-prehensile manipulation. The controller is a
recurrent neural network using raw images as input and generating robot arm
trajectories, with the parameters shared across the tasks. The controller also
combines VAE-GAN-based reconstruction with autoregressive multimodal action
prediction. Our results demonstrate that it is possible to learn complex
manipulation tasks, such as picking up a towel, wiping an object, and
depositing the towel to its previous position, entirely from raw images with
direct behavior cloning. We show that weight sharing and reconstruction-based
regularization substantially improve generalization and robustness, and
training on multiple tasks simultaneously increases the success rate on all
tasks
Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression
We design a new approach that allows robot learning of new activities from
unlabeled human example videos. Given videos of humans executing the same
activity from a human's viewpoint (i.e., first-person videos), our objective is
to make the robot learn the temporal structure of the activity as its future
regression network, and learn to transfer such model for its own motor
execution. We present a new deep learning model: We extend the state-of-the-art
convolutional object detection network for the representation/estimation of
human hands in training videos, and newly introduce the concept of using a
fully convolutional network to regress (i.e., predict) the intermediate scene
representation corresponding to the future frame (e.g., 1-2 seconds later).
Combining these allows direct prediction of future locations of human hands and
objects, which enables the robot to infer the motor control plan using our
manipulation network. We experimentally confirm that our approach makes
learning of robot activities from unlabeled human interaction videos possible,
and demonstrate that our robot is able to execute the learned collaborative
activities in real-time directly based on its camera input
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors
One of the key challenges in applying reinforcement learning to complex
robotic control tasks is the need to gather large amounts of experience in
order to find an effective policy for the task at hand. Model-based
reinforcement learning can achieve good sample efficiency, but requires the
ability to learn a model of the dynamics that is good enough to learn an
effective policy. In this work, we develop a model-based reinforcement learning
algorithm that combines prior knowledge from previous tasks with online
adaptation of the dynamics model. These two ingredients enable highly
sample-efficient learning even in regimes where estimating the true dynamics is
very difficult, since the online model adaptation allows the method to locally
compensate for unmodeled variation in the dynamics. We encode the prior
experience into a neural network dynamics model, adapt it online by
progressively refitting a local linear model of the dynamics, and use model
predictive control to plan under these dynamics. Our experimental results show
that this approach can be used to solve a variety of complex robotic
manipulation tasks in just a single attempt, using prior data from other
manipulation behaviors
- …