Large language models have demonstrated impressive capabilities across
various natural language processing tasks, especially in solving mathematical
problems. However, large language models are not good at math theorem proving
using formal languages like Lean. A significant challenge in this area is the
scarcity of training data available in these formal languages. To address this
issue, we propose a novel pipeline that iteratively generates and filters
synthetic data to translate natural language mathematical problems into Lean 4
statements, and vice versa. Our results indicate that the synthetic data
pipeline can provide useful training data and improve the performance of LLMs
in translating and understanding complex mathematical problems and proofs. Our
final dataset contains about 57K formal-informal question pairs along with
searched proof from the math contest forum and 21 new IMO questions. We
open-source our code at this https URL and our data
at this https URL

通过提出一种新的流程，我们利用合成数据来将自然语言数学问题转化为 Lean 4 语句，并相应地进行过滤，从而为解决 LLMs 在理解复杂数学问题和证明上的性能提供有用的训练数据。最终数据集包含约 57K 个正式 - 非正式问题对以及来自数学竞赛论坛的搜索证明和 21 个新的 IMO 问题。

精益练习册：从自然语言数学问题转化而成的大规模精益问题集

Lean Workbook: A large-scale Lean problem set formalized from natural  language math problems

Developing algorithms for extra-terrestrial robotic exploration has always
been challenging. Along with the complexity associated with these environments,
one of the main issues remains the evaluation of said algorithms. With the
regained interest in lunar exploration, there is also a demand for quality
simulators that will enable the development of lunar robots. % In this paper,
we explain how we built a Lunar simulator based on Isaac Sim, Nvidia's robotic
simulator. In this paper, we propose Omniverse Lunar Robotic-Sim (OmniLRS) that
is a photorealistic Lunar simulator based on Nvidia's robotic simulator. This
simulation provides fast procedural environment generation, multi-robot
capabilities, along with synthetic data pipeline for machine-learning
applications. It comes with ROS1 and ROS2 bindings to control not only the
robots, but also the environments. This work also performs sim-to-real rock
instance segmentation to show the effectiveness of our simulator for
image-based perception. Trained on our synthetic data, a yolov8 model achieves
performance close to a model trained on real-world data, with 5% performance
gap. When finetuned with real data, the model achieves 14% higher average
precision than the model trained on real-world data, demonstrating our
simulator's photorealism.% to realize sim-to-real. The code is fully
open-source, accessible here: this https URL, and
comes with demonstrations.

基于 Nvidia 的机器人模拟器，我们构建了一个基于 Isaac Sim 的月球模拟器，名为 Omniverse Lunar Robotic-Sim（OmniLRS）。该模拟器提供快速的程序化环境生成、多机器人能力以及机器学习应用的合成数据管道。通过我们的合成数据进行训练，yolov8 模型在实例分割方面表现接近于在真实数据上训练的模型，性能差距为 5%。在使用真实数据进行微调后，该模型的平均精度比在真实世界数据上训练的模型高 14%，证明了我们模拟器的真实感。

OmniLRS：月球机器人的逼真模拟器

OmniLRS: A Photorealistic Simulator for Lunar Robotics

We address the problem of generating realistic 3D motions of humans
interacting with objects in a scene. Our key idea is to create a neural
interaction field attached to a specific object, which outputs the distance to
the valid interaction manifold given a human pose as input. This interaction
field guides the sampling of an object-conditioned human motion diffusion
model, so as to encourage plausible contacts and affordance semantics. To
support interactions with scarcely available data, we propose an automated
synthetic data pipeline. For this, we seed a pre-trained motion model, which
has priors for the basics of human movement, with interaction-specific anchor
poses extracted from limited motion capture data. Using our guided diffusion
model trained on generated synthetic data, we synthesize realistic motions for
sitting and lifting with several objects, outperforming alternative approaches
in terms of motion quality and successful action completion. We call our
framework NIFTY: Neural Interaction Fields for Trajectory sYnthesis.

本文提出用于人类与场景中物体进行交互的逼真三维运动生成方法，提出了一种基于神经交互场的技术，该技术可以指导对象条件化的人类运动扩散模型的抽样，以鼓励合理接触和可发掘意义，在支持使用少有数据的情况下，用自动合成数据流水线进行这项研究，最终提出了一种名为 NIFTY（神经交互场用于轨迹综合）的框架。