DINO+CDP Tomato Harvesting Dataset

This repository contains a high-fidelity teleoperated robotic manipulation dataset designed to support imitation learning, multi-view perception, and autonomous agricultural manipulation tasks—specifically tomato plant pruning and harvesting. The data captures real-world robot-plant interactions collected in a controlled biocell greenhouse chamber under consistent lighting conditions. Data Collection & Hardware Setup The dataset was generated using a dual-manipulator setup in a leader–follower configuration consisting of two MyCobot M5-280 robotic manipulators. As an operator teleoperated the leader robot (actor), the follower robot (imitator) mirrored its movements. Dataset Structure & Specifications The dataset comprises 100 total demonstrations sampled at a temporal frequency of 20 Hz (0.05-second intervals). Each individual demonstration consists of 300 temporally aligned timesteps containing synchronized visual and kinematic data streams: • Kinematic Data: 6 synchronized joint angles from the actor (leader) robot and 6 synchronized joint angles from the imitator (follower) robot. • Visual Data: Multi-view RGB camera streams captured simultaneously from three distinct viewpoints focusing on the agricultural interaction area. • Cultivar Variations: The dataset spans multiple tomato cultivars, including Celebrity, Beefsteak, and Big Boy Hybrid. Target Applications This data is structured to train and evaluate learning-based robotic frameworks, including self-supervised representation learning models (such as DINO) and policy learning frameworks (such as Conditional Diffusion Policies) for agricultural automation.本仓库包含一套高保真遥操作机器人操控数据集，旨在支持模仿学习、多视图感知以及自主农业操控任务——具体为番茄植株修剪与采摘作业。该数据采集自受控生物舱温室环境，在恒定光照条件下记录了真实的机器人与植株交互过程。 数据采集与硬件配置 本数据集采用领导者-跟随者（leader–follower）构型的双机械臂系统生成，具体使用两台MyCobot M5-280型机械臂。当操作员遥操作主机械臂（操控端）时，从机械臂（模仿端）会同步复刻其运动轨迹。 数据集结构与参数规格 本数据集共包含100组演示数据，采样频率为20 Hz（时间间隔0.05秒）。每组独立演示数据包含300个时间对齐的时间步，涵盖同步的视觉与运动学数据流： • 运动学数据：包含主机械臂（操控端）的6组同步关节角度，以及从机械臂（模仿端）的6组同步关节角度。 • 视觉数据：从三个不同视角同时采集的多视图RGB相机流，聚焦于农业交互区域。 • 品种多样性：本数据集覆盖多个番茄品种，包括Celebrity、Beefsteak以及Big Boy Hybrid。 目标应用场景 本数据集的结构设计用于训练与评估基于学习的机器人框架，包括用于农业自动化的自监督表征学习模型（如DINO）以及策略学习框架（如条件扩散策略（Conditional Diffusion Policies））。