DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Główne pojęcia

Training with DROID leads to policies with higher performance, greater robustness, and improved generalization ability.

Streszczenie

Abstract: Introduces DROID, a diverse robot manipulation dataset with 76k trajectories collected across 564 scenes.
Introduction: Highlights the importance of diverse datasets for training generalizable robot manipulation policies.
Data Extraction: Mentions key metrics like 76k demonstration trajectories and 350 hours of interaction data.
Experiments: Showcases how training with DROID improves policy performance and robustness across various tasks.
Discussion: Emphasizes the significance of DROID in advancing research on robot manipulation policies.

Statystyki

Each DROID episode contains three synchronized RGB camera streams, depth information, and natural language instructions.
DROID consists of 76k demonstration trajectories or 350 hours of interaction data collected across 564 scenes and 86 tasks.

Cytaty

"We introduce DROID (Distributed Robot Interaction Dataset), an “in-the-wild” robot manipulation dataset."
"Training with DROID leads to policies with higher performance, greater robustness, and improved generalization ability."

Kluczowe wnioski z

DROID

by Alexander Kh... o arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12945.pdf

Głębsze pytania

How can the diversity in scene types impact the generalization abilities of robot manipulation policies?

The diversity in scene types plays a crucial role in enhancing the generalization abilities of robot manipulation policies. When training robotic systems on datasets with a wide range of scene types, it exposes them to various environmental conditions, layouts, and objects. This exposure helps the models learn robust features that are applicable across different scenarios. By encountering diverse scenes during training, the robot learns to adapt its actions and decisions based on varying contexts, leading to improved performance when faced with new or unseen environments. Additionally, diverse scene types challenge the model to generalize its learned behaviors beyond specific settings, promoting flexibility and adaptability in real-world applications.

What are the potential challenges in collecting large-scale robot manipulation datasets outside controlled environments?

Collecting large-scale robot manipulation datasets outside controlled environments presents several challenges. One significant challenge is ensuring data quality and consistency across diverse settings. In uncontrolled environments, factors such as lighting conditions, object variations, background clutter, and unexpected obstacles can introduce noise into the dataset, affecting model performance. Safety concerns also arise when operating robots in unfamiliar or dynamic surroundings where there may be hazards or unpredictable elements.
Logistical issues like coordinating data collection efforts across multiple locations and managing hardware setups at scale can pose logistical challenges. Ensuring standardization in data collection protocols becomes more complex when dealing with distributed teams collecting data independently.
Moreover, scaling up data collection efforts outside controlled environments requires substantial investments in resources including hardware setup maintenance, supervision by human operators for safety reasons (especially during teleoperation), and potentially longer timeframes due to unforeseen circumstances that may arise during field operations.

How might leveraging diverse datasets like DROID improve visual representations for robotic control?

Leveraging diverse datasets like DROID can significantly enhance visual representations for robotic control through exposure to a wide array of scenes and tasks. By training on a dataset with varied viewpoints captured from different camera angles within distinct scenes containing numerous objects and interactions,
robotic systems can learn rich visual features that generalize well across different scenarios.
The diversity present in DROID allows models to capture intricate details about object shapes,
sizes,and textures under various lighting conditions which contributes towards learning robust
visual representations.
Additionally,the inclusion of natural language instructions alongside visual inputs enables
the development of multimodal representation learning techniques that fuse linguistic context
with visual cues.This fusion enhances semantic understanding,promoting more informed decision-making processes by robots.
Overall,DROID's diverse dataset serves as a valuable resource for improving not only policy learning but also advancing research on multi-modal representation learning for enhanced robotic control capabilities.