Omkar2118/datasets huggingface dataset:
BEHAVIOR-1K

The **BEHAVIOR-1K** dataset is a comprehensive simulation benchmark for human-centered robotics¹. It is more grounded on actual human needs compared to its predecessor, BEHAVIOR-100¹. The 1,000 activities in the dataset come from the results of an extensive survey on "what do you want robots to do for you?"¹.

Here are some key features of the BEHAVIOR-1K dataset:
- It includes **1,000 everyday activities**, which are grounded in **50 scenes** (houses, gardens, restaurants, offices, etc.) with more than **9,000 objects** annotated with rich physical and semantic properties¹.
- It uses **OmniGibson** (based on Nvidia’s Omniverse), a novel simulation environment that supports these activities via realistic physics simulation and rendering of rigid bodies, deformable bodies, and liquids¹.
- The activities, scenes, and objects in the dataset are defined in the **Behavior Domain Definition Language (BDDL)**¹.
- The dataset is designed to be more diverse in the type of scenes, objects, and activities¹.
- It achieves a new level of realism in rendering and physics simulation, powered by NVIDIA’s Omniverse¹.

This dataset is valuable for embodied AI and robot learning research¹. It provides a robust definition of the 1,000 activities over 50 diverse scenes constituting over 9,000 objects⁵. Each object and scene in the dataset is annotated with considerable detail to ensure simulations can be as close to real-world scenarios as possible⁵.

(1) BEHAVIOR-1K – BEHAVIOR - Stanford University. https://behavior.stanford.edu/behavior-1k.
(2) 2403.09227 - BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with .... https://www.emergentmind.com/papers/2403.09227.
(3) BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 .... https://arxiv.org/html/2403.09227v1.
(4) 【具身智能评估8】BEHAVIOR-1K: A Benchmark for Embodied AI with 1,000 Everyday .... https://blog.csdn.net/weixin_39653948/article/details/135074209.
(5) stanford Behavior-1k——包含一千种日常任务的具身智能benchmark - 知乎. https://zhuanlan.zhihu.com/p/669737780.

Introducing paper: BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
RMOT-223

In this dataset, various objects are arranged on a white table. A UR5e robot picks and place a target object specified on the title of the video/image sequence. Videos under auto- folder are collected with automatic operation of the robot. Videos under human- folders are collected with the tele-operation of the robot. Ground-truth tracking bounding boxes are generated with STARK, and when the target exits the camera frame, the bounding box estimation is switched to [-1, -1, -1, -1], indicating target not shown.

Introducing paper: EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object