402 research outputs found
Learning to Use Chopsticks in Diverse Gripping Styles
Learning dexterous manipulation skills is a long-standing challenge in
computer graphics and robotics, especially when the task involves complex and
delicate interactions between the hands, tools and objects. In this paper, we
focus on chopsticks-based object relocation tasks, which are common yet
demanding. The key to successful chopsticks skills is steady gripping of the
sticks that also supports delicate maneuvers. We automatically discover
physically valid chopsticks holding poses by Bayesian Optimization (BO) and
Deep Reinforcement Learning (DRL), which works for multiple gripping styles and
hand morphologies without the need of example data. Given as input the
discovered gripping poses and desired objects to be moved, we build
physics-based hand controllers to accomplish relocation tasks in two stages.
First, kinematic trajectories are synthesized for the chopsticks and hand in a
motion planning stage. The key components of our motion planner include a
grasping model to select suitable chopsticks configurations for grasping the
object, and a trajectory optimization module to generate collision-free
chopsticks trajectories. Then we train physics-based hand controllers through
DRL again to track the desired kinematic trajectories produced by the motion
planner. We demonstrate the capabilities of our framework by relocating objects
of various shapes and sizes, in diverse gripping styles and holding positions
for multiple hand morphologies. Our system achieves faster learning speed and
better control robustness, when compared to vanilla systems that attempt to
learn chopstick-based skills without a gripping pose optimization module and/or
without a kinematic motion planner
Numerical Simulations of Spread Characteristics of Toxic Cyanide in the Danjiangkou Reservoir in China under the Effects of Dam Cooperation
Many accidents of releasing toxic pollutants into surface water happen each year in the world. It is believed that dam cooperation can affect flow field in reservoir and then can be applied to avoiding and reducing spread speed of toxic pollutants to drinking water intake mouth. However, few studies investigated the effects of dam cooperation on the spread characteristics of toxic pollutants in reservoir, especially the source reservoir for water diversion with more than one dam. The Danjiangkou Reservoir is the source reservoir of the China’ South-to-North Water Diversion Middle Route Project. The human activities are active within this reservoir basin and cyanide-releasing accident once happened in upstream inflow. In order to simulate the spread characteristics of cyanide in the reservoir in the condition of dam cooperation, a three-dimensional water quality model based on the Environmental Fluid Dynamics Code (EFDC) has been built and put into practice. The results indicated that cooperation of two dams of the Danjiangkou Reservoir could be applied to avoiding and reducing the spread speed of toxic cyanide in the reservoir directing to the water intake mouth for water diversions
Privacy-preserving Energy Scheduling for Smart Grid with Renewables
We consider joint demand response and power procurement to optimize the average social welfare of a smart power grid system with renewable sources. The renewable sources such as wind and solar energy are intermittent and fluctuate rapidly. As a consequence, the demand response algorithm needs to be executed in real time to ensure the stability of a smart grid system with renewable sources. We develop a demand response algorithm that converges to the optimal solution with superlinear rates of convergence. In the simulation studies, the proposed algorithm converges roughly thirty time faster than the traditional subgradient algorithm. In addition, it is fully distributed and can be realized either synchronously or in asynchronous manner, which eases practical deployment
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