In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
The physical design of a robot and the policy that controls its motion are inherently coupled. However, existing approaches largely ignore this coupling, instead choosing to alternate between separate design and control phases, which requires expert intuition throughout and risks convergence to suboptimal designs. In this work, we propose a method that jointly optimizes over the physical design of a robot and the corresponding control policy in a model-free fashion, without any need for expert supervision. Given an arbitrary robot morphology, our method maintains a distribution over the design parameters and uses reinforcement learning to train a neural network controller. Throughout training, we refine the robot distribution to maximize the expected reward. This results in an assignment to the robot parameters and neural network policy that are jointly optimal. We evaluate our approach in the context of legged locomotion, and demonstrate that it discovers novel robot designs and walking gaits for several different morphologies, achieving performance comparable to or better than that of hand-crafted designs.

Ex Output:
Use deep reinforcement learning to design the physical attributes of a robot jointly with a control policy.


Ex Input:
Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attentional mechanisms into neural networks increases the performance of the system substantially. We report on a new modular network architecture that applies an attentional mechanism not on temporal and spatial regions of the input, but on sensor selection for multi-sensor setups. This network called the sensor transformation attention network (STAN) is evaluated in scenarios which include the presence of natural noise or synthetic dynamic noise. We demonstrate how the attentional signal responds dynamically to changing noise levels and sensor-specific noise, leading to reduced word error rates (WERs) on both audio and visual tasks using TIDIGITS and GRID; and also on CHiME-3, a multi-microphone real-world noisy dataset. The improvement grows as more channels are corrupted as demonstrated on the CHiME-3 dataset. Moreover, the proposed STAN architecture naturally introduces a number of advantages including ease of removing sensors from existing architectures, attentional interpretability, and increased robustness to a variety of noise environments.

Ex Output:
We introduce a modular multi-sensor network architecture with an attentional mechanism that enables dynamic sensor selection on real-world noisy data from CHiME-3.


Ex Input:
Neural Style Transfer has become a popular technique for
 generating images of distinct artistic styles using convolutional neural networks. This
 recent success in image style transfer has raised the question of
 whether similar methods can be leveraged to alter the “style” of musical
 audio. In this work, we attempt long time-scale high-quality audio transfer
 and texture synthesis in the time-domain that captures harmonic,
 rhythmic, and timbral elements related to musical style, using examples that
 may have different lengths and musical keys. We demonstrate the ability
 to use randomly initialized convolutional neural networks to transfer
 these aspects of musical style from one piece onto another using 3
 different representations of audio: the log-magnitude of the Short Time
 Fourier Transform (STFT), the Mel spectrogram, and the Constant-Q Transform
 spectrogram. We propose using these representations as a way of
 generating and modifying perceptually significant characteristics of
 musical audio content. We demonstrate each representation's
 shortcomings and advantages over others by carefully designing
 neural network structures that complement the nature of musical audio. Finally, we show that the most
 compelling “style” transfer examples make use of an ensemble of these
 representations to help capture the varying desired characteristics of
 audio signals.

Ex Output:

We present a long time-scale musical audio style transfer algorithm which synthesizes audio in the time-domain, but uses Time-Frequency representations of audio.