Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control (1812.00568v1)

Abstract: Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains. We present a deep RL method that is practical for real-world robotics tasks, such as robotic manipulation, and generalizes effectively to never-before-seen tasks and objects. In these settings, ground truth reward signals are typically unavailable, and we therefore propose a self-supervised model-based approach, where a predictive model learns to directly predict the future from raw sensory readings, such as camera images. At test time, we explore three distinct goal specification methods: designated pixels, where a user specifies desired object manipulation tasks by selecting particular pixels in an image and corresponding goal positions, goal images, where the desired goal state is specified with an image, and image classifiers, which define spaces of goal states. Our deep predictive models are trained using data collected autonomously and continuously by a robot interacting with hundreds of objects, without human supervision. We demonstrate that visual MPC can generalize to never-before-seen objects---both rigid and deformable---and solve a range of user-defined object manipulation tasks using the same model.

An Evaluation of the IEEEtran.cls Utilization for IEEE Computer Society Journals

The paper "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals", authored by Michael Shell, John Doe, and Jane Doe, presents a practical demonstration of employing the IEEEtran.cls class file for producing academic papers specific to IEEE Computer Society publications. This document serves a dual purpose: firstly, as an introductory blueprint for new users of the LaTeX document preparation system within the IEEE framework, and secondly, as a guideline for embedding essential IEEE publication standards in computational documentation.

The authors elucidate the structural intricacies and formatting requisites mandated by IEEE journals, particularly focusing on the IEEEtran.cls version 1.8b and later. This deliverable assumes significance given the IEEE's global stature, compelling researchers to strictly conform to its editorial policies and presentation formats. In this context, the document not only facilitates users in navigation through technical templates but also ensures compliance with IEEE's stylistic and typographic benchmarks.

The introduction of the paper underscores a pragmatic approach towards familiarizing LaTeX users with the nuances of IEEE-format paper authoring. By providing explicit code examples incorporated within the text, this documentation aids researchers in overcoming common hurdles encountered during document formatting. The inclusion of sections, subsections, appendices, and references serves a practical utility in showcasing the capabilities of the template across different structural elements of an academic paper.

While the paper lacks experimental or empirical results typically expected in research manuscripts, its contribution lies in its utility rather than in novel insights into a specific scientific problem. The implications of the paper extend predominantly to those involved in academic publishing within the technical purview of IEEE journals. By streamlining the document preparation process, it potentially accelerates the timeline from manuscript development to submission, thereby indirectly augmenting the efficiency of scholarly dissemination.

Practical applications of the insights offered in this paper are primarily constrained to academic environments where IEEE's document standards are pertinent. The educational merit of this document stands to benefit both novice and seasoned researchers who require a concise yet comprehensive guide to navigating IEEE's publication procedures via LaTeX.

In terms of future developments, this document could benefit from integration with evolving digital tools and platforms that automate document formatting processes. Additionally, a more interactive or visual interface showing real-time template applications could further enhance user engagement within an evolving scholarly communication landscape.

In conclusion, while "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals" is not a research paper in the conventional sense, it plays a crucial role in the sphere of academic publishing by advancing the usability of LaTeX through standardization templates. This document might serve as a foundational resource for effectively marrying computational efficiency with academic rigor in IEEE journal submissions.