964 research outputs found
Pando: Personal Volunteer Computing in Browsers
The large penetration and continued growth in ownership of personal
electronic devices represents a freely available and largely untapped source of
computing power. To leverage those, we present Pando, a new volunteer computing
tool based on a declarative concurrent programming model and implemented using
JavaScript, WebRTC, and WebSockets. This tool enables a dynamically varying
number of failure-prone personal devices contributed by volunteers to
parallelize the application of a function on a stream of values, by using the
devices' browsers. We show that Pando can provide throughput improvements
compared to a single personal device, on a variety of compute-bound
applications including animation rendering and image processing. We also show
the flexibility of our approach by deploying Pando on personal devices
connected over a local network, on Grid5000, a French-wide computing grid in a
virtual private network, and seven PlanetLab nodes distributed in a wide area
network over Europe.Comment: 14 pages, 12 figures, 2 table
Reinforcement Learning Experience Reuse with Policy Residual Representation
Experience reuse is key to sample-efficient reinforcement learning. One of
the critical issues is how the experience is represented and stored.
Previously, the experience can be stored in the forms of features, individual
models, and the average model, each lying at a different granularity. However,
new tasks may require experience across multiple granularities. In this paper,
we propose the policy residual representation (PRR) network, which can extract
and store multiple levels of experience. PRR network is trained on a set of
tasks with a multi-level architecture, where a module in each level corresponds
to a subset of the tasks. Therefore, the PRR network represents the experience
in a spectrum-like way. When training on a new task, PRR can provide different
levels of experience for accelerating the learning. We experiment with the PRR
network on a set of grid world navigation tasks, locomotion tasks, and fighting
tasks in a video game. The results show that the PRR network leads to better
reuse of experience and thus outperforms some state-of-the-art approaches.Comment: Conference version appears in IJCAI 201
Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning
The next generation of intelligent robots will need to be able to plan reaches. Not just ballistic point to point reaches, but reaches around things such as the edge of a table, a nearby human, or any other known object in the robot’s workspace. Planning reaches may seem easy to us humans, because we do it so intuitively, but it has proven to be a challenging problem, which continues to limit the versatility of what robots can do today. In this document, I propose a novel intrinsically motivated RL system that draws on both Path/Motion Planning and Reactive Control. Through Reinforcement Learning, it tightly integrates these two previously disparate approaches to robotics. The RL system is evaluated on a task, which is as yet unsolved by roboticists in practice. That is to put the palm of the iCub humanoid robot on arbitrary target objects in its workspace, start- ing from arbitrary initial configurations. Such motions can be generated by planning, or searching the configuration space, but this typically results in some kind of trajectory, which must then be tracked by a separate controller, and such an approach offers a brit- tle runtime solution because it is inflexible. Purely reactive systems are robust to many problems that render a planned trajectory infeasible, but lacking the capacity to search, they tend to get stuck behind constraints, and therefore do not replace motion planners. The planner/controller proposed here is novel in that it deliberately plans reaches without the need to track trajectories. Instead, reaches are composed of sequences of reactive motion primitives, implemented by my Modular Behavioral Environment (MoBeE), which provides (fictitious) force control with reactive collision avoidance by way of a realtime kinematic/geometric model of the robot and its workspace. Thus, to the best of my knowledge, mine is the first reach planning approach to simultaneously offer the best of both the Path/Motion Planning and Reactive Control approaches. By controlling the real, physical robot directly, and feeling the influence of the con- straints imposed by MoBeE, the proposed system learns a stochastic model of the iCub’s configuration space. Then, the model is exploited as a multiple query path planner to find sensible pre-reach poses, from which to initiate reaching actions. Experiments show that the system can autonomously find practical reaches to target objects in workspace and offers excellent robustness to changes in the workspace configuration as well as noise in the robot’s sensory-motor apparatus
2004 Research Engineering Annual Report
Selected research and technology activities at Dryden Flight Research Center are summarized. These activities exemplify the Center's varied and productive research efforts
X-24C research vehicle
A group of experiments that might be accomplished on the X-24C research vehicle are discussed indicating in each case the technology development needed to ready the experiments for flight, and also indicating interface problems between the vehicle and the experiment. Experiments that could be cheaply done using test platforms other than the X-24C have been eliminated. Experiments that are clearly applicable only to the X-24C research vehicle are, of course, included. Experiments that might be accomplished on either the X-24C or some other platform requiring further investigation concerning proper applicability are included for consideration
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