207 research outputs found
Should I send now or send later? A decision-theoretic approach to transmission scheduling in sensor networks with mobile sinks
Mobile sinks can significantly extend the lifetime of a sensor network by eliminating the need for expensive hop-by-hop routing. However, a sensor node might not always have a mobile sink in transmission range, or the mobile sink might be so far that the data transmission would be very expensive. In the latter case, the sensor node needs to make a decision whether it should send the data now, or take the risk to wait for a more favorable occasion. Making the right decisions in this transmission scheduling problem has significant impact on the performance and lifetime of the node. In this paper, we investigate the fundamentals of the transmission scheduling problem for sensor networks with mobile sinks. We first develop a dynamic programming-based optimal algorithm for the case when the mobility of the sinks is known in advance. Then, we describe two decision theoretic algorithms which use only probabilistic models learned from the history of interaction with the mobile sinks, and do not require knowledge about their future mobility patterns. The first algorithm uses Markov Decision Processes with states without history information, while the second algorithm encodes some elements of the history into the state. Through a series of experiments, we show that the decision theoretic approaches significantly outperform naive heuristics, and can have a performance close to that of the optimal approach, without requiring an advance knowledge of the mobility
From virtual demonstration to real-world manipulation using LSTM and MDN
Robots assisting the disabled or elderly must perform complex manipulation
tasks and must adapt to the home environment and preferences of their user.
Learning from demonstration is a promising choice, that would allow the
non-technical user to teach the robot different tasks. However, collecting
demonstrations in the home environment of a disabled user is time consuming,
disruptive to the comfort of the user, and presents safety challenges. It would
be desirable to perform the demonstrations in a virtual environment. In this
paper we describe a solution to the challenging problem of behavior transfer
from virtual demonstration to a physical robot. The virtual demonstrations are
used to train a deep neural network based controller, which is using a Long
Short Term Memory (LSTM) recurrent neural network to generate trajectories. The
training process uses a Mixture Density Network (MDN) to calculate an error
signal suitable for the multimodal nature of demonstrations. The controller
learned in the virtual environment is transferred to a physical robot (a
Rethink Robotics Baxter). An off-the-shelf vision component is used to
substitute for geometric knowledge available in the simulation and an inverse
kinematics module is used to allow the Baxter to enact the trajectory. Our
experimental studies validate the three contributions of the paper: (1) the
controller learned from virtual demonstrations can be used to successfully
perform the manipulation tasks on a physical robot, (2) the LSTM+MDN
architectural choice outperforms other choices, such as the use of feedforward
networks and mean-squared error based training signals and (3) allowing
imperfect demonstrations in the training set also allows the controller to
learn how to correct its manipulation mistakes
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
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