36,790 research outputs found
Humanoid Motion Description Language
In this paper we propose a description language for specifying motions for humanoid robots and for allowing humanoid robots to acquire motor skills. Locomotion greatly increases our ability to interact with our environments, which in turn increases our mental abilities. This principle also applies to humanoid robots. However, there are great difficulties to specify humanoid motions and to represent motor skills, which in most cases require four-dimensional space representations. We propose a representation framework that includes the following attributes: motion description layers, egocentric reference system, progressive quantized refinement, and automatic constraint satisfaction. We also outline strategies for acquiring new motor skills by learning from trial and error, macro approach, and programming. Then, we outline the development of a new humanoid motion description language called Cybele
A Massive Data Parallel Computational Framework for Petascale/Exascale Hybrid Computer Systems
Heterogeneous systems are becoming more common on High Performance Computing
(HPC) systems. Even using tools like CUDA and OpenCL it is a non-trivial task
to obtain optimal performance on the GPU. Approaches to simplifying this task
include Merge (a library based framework for heterogeneous multi-core systems),
Zippy (a framework for parallel execution of codes on multiple GPUs), BSGP (a
new programming language for general purpose computation on the GPU) and
CUDA-lite (an enhancement to CUDA that transforms code based on annotations).
In addition, efforts are underway to improve compiler tools for automatic
parallelization and optimization of affine loop nests for GPUs and for
automatic translation of OpenMP parallelized codes to CUDA.
In this paper we present an alternative approach: a new computational
framework for the development of massively data parallel scientific codes
applications suitable for use on such petascale/exascale hybrid systems built
upon the highly scalable Cactus framework. As the first non-trivial
demonstration of its usefulness, we successfully developed a new 3D CFD code
that achieves improved performance.Comment: Parallel Computing 2011 (ParCo2011), 30 August -- 2 September 2011,
Ghent, Belgiu
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers
Recent developments in engineering and algorithms have made real-world
applications in quantum computing possible in the near future. Existing quantum
programming languages and compilers use a quantum assembly language composed of
1- and 2-qubit (quantum bit) gates. Quantum compiler frameworks translate this
quantum assembly to electric signals (called control pulses) that implement the
specified computation on specific physical devices. However, there is a
mismatch between the operations defined by the 1- and 2-qubit logical ISA and
their underlying physical implementation, so the current practice of directly
translating logical instructions into control pulses results in inefficient,
high-latency programs. To address this inefficiency, we propose a universal
quantum compilation methodology that aggregates multiple logical operations
into larger units that manipulate up to 10 qubits at a time. Our methodology
then optimizes these aggregates by (1) finding commutative intermediate
operations that result in more efficient schedules and (2) creating custom
control pulses optimized for the aggregate (instead of individual 1- and
2-qubit operations). Compared to the standard gate-based compilation, the
proposed approach realizes a deeper vertical integration of high-level quantum
software and low-level, physical quantum hardware. We evaluate our approach on
important near-term quantum applications on simulations of superconducting
quantum architectures. Our proposed approach provides a mean speedup of
, with a maximum of . Because latency directly affects the
feasibility of quantum computation, our results not only improve performance
but also have the potential to enable quantum computation sooner than otherwise
possible.Comment: 13 pages, to apper in ASPLO
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