18,965 research outputs found
A 3D simulator for intelligent environment experiments
The advances in sensor networks, electronics and ambient intelligence make creation of intelligent environments (IEs) possible. However, on account of economic and logistic issues the implementation of physical IEs is difficult in research domain. That makes it harder for researchers to experiment new approaches in IE domain. In this article, we propose a simulator to build virtual IEs. Simulators are a good alternative to physical IEs. Indeed, virtual IEs does not require expensive resources. Moreover, researchers and designers can conduct experiments anytime and repeat scenarios easily. Our simulator provides users with a set of virtual sensors and actuators. Our virtual sensors try to reproduce behavior of physical sensors and to produce datasets with the same properties as those generated by real sensors. Our proposition contains a tool to build a home from scratch and a model to define scenarios and behaviors of occupants. It also proposes an interface to control occupants directly. Virtual sensors collect data and generate datasets. Scientists and designers can use these datasets to evaluate and design new approaches in IE domain
Reasoning About Liquids via Closed-Loop Simulation
Simulators are powerful tools for reasoning about a robot's interactions with
its environment. However, when simulations diverge from reality, that reasoning
becomes less useful. In this paper, we show how to close the loop between
liquid simulation and real-time perception. We use observations of liquids to
correct errors when tracking the liquid's state in a simulator. Our results
show that closed-loop simulation is an effective way to prevent large
divergence between the simulated and real liquid states. As a direct
consequence of this, our method can enable reasoning about liquids that would
otherwise be infeasible due to large divergences, such as reasoning about
occluded liquid.Comment: Robotics: Science & Systems (RSS), July 12-16, 2017. Cambridge, MA,
US
DoorGym: A Scalable Door Opening Environment And Baseline Agent
In order to practically implement the door opening task, a policy ought to be
robust to a wide distribution of door types and environment settings.
Reinforcement Learning (RL) with Domain Randomization (DR) is a promising
technique to enforce policy generalization, however, there are only a few
accessible training environments that are inherently designed to train agents
in domain randomized environments. We introduce DoorGym, an open-source door
opening simulation framework designed to utilize domain randomization to train
a stable policy. We intend for our environment to lie at the intersection of
domain transfer, practical tasks, and realism. We also provide baseline
Proximal Policy Optimization and Soft Actor-Critic implementations, which
achieves success rates between 0% up to 95% for opening various types of doors
in this environment. Moreover, the real-world transfer experiment shows the
trained policy is able to work in the real world. Environment kit available
here: https://github.com/PSVL/DoorGym/Comment: Full version (Real world transfer experiments result
Torque-Controlled Stepping-Strategy Push Recovery: Design and Implementation on the iCub Humanoid Robot
One of the challenges for the robotics community is to deploy robots which
can reliably operate in real world scenarios together with humans. A crucial
requirement for legged robots is the capability to properly balance on their
feet, rejecting external disturbances. iCub is a state-of-the-art humanoid
robot which has only recently started to balance on its feet. While the current
balancing controller has proved successful in various scenarios, it still
misses the capability to properly react to strong pushes by taking steps. This
paper goes in this direction. It proposes and implements a control strategy
based on the Capture Point concept [1]. Instead of relying on position control,
like most of Capture Point related approaches, the proposed strategy generates
references for the momentum-based torque controller already implemented on the
iCub, thus extending its capabilities to react to external disturbances, while
retaining the advantages of torque control when interacting with the
environment. Experiments in the Gazebo simulator and on the iCub humanoid robot
validate the proposed strategy
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