REVIEW 5 cited by
Robo360: A 3D Omnispective Multi-Material Robotic Manipulation Dataset
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Robo360: A 3D Omnispective Multi-Material Robotic Manipulation Dataset
read the original abstract
Building robots that can automate labor-intensive tasks has long been the core motivation behind the advancements in computer vision and the robotics community. Recent interest in leveraging 3D algorithms, particularly neural fields, has led to advancements in robot perception and physical understanding in manipulation scenarios. However, the real world's complexity poses significant challenges. To tackle these challenges, we present Robo360, a dataset that features robotic manipulation with a dense view coverage, which enables high-quality 3D neural representation learning, and a diverse set of objects with various physical and optical properties and facilitates research in various object manipulation and physical world modeling tasks. We confirm the effectiveness of our dataset using existing dynamic NeRF and evaluate its potential in learning multi-view policies. We hope that Robo360 can open new research directions yet to be explored at the intersection of understanding the physical world in 3D and robot control.
Forward citations
Cited by 5 Pith papers
-
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Deform360 supplies 215+ hours of synchronized multi-view video and tactile data plus markerless 3D tracks, revealing that 3D particle models win in low data while 2D video models generalize better at scale.
-
CAGS: Color-Adaptive Volumetric Video Streaming with Dynamic 3D Gaussian Splatting
CAGS delivers 5-20 dB higher PSNR in volumetric video streaming by applying a color-adaptive scheme with vector quantization on 3D Gaussians and server-rendered reference images for client-side color restoration.
-
ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
ABot-M0 unifies heterogeneous robot data into a 6-million-trajectory dataset and introduces Action Manifold Learning to predict stable actions on a low-dimensional manifold using a DiT backbone.
-
World Models for Robotic Manipulation: A Survey
Survey organizing world models for robotic manipulation into representation families, a functional taxonomy, and infrastructure roles across pretraining, post-training, and inference, while reviewing 34 datasets and e...
-
World Action Models: A Survey
A survey that clarifies boundaries and organizes World Action Models by generation requirements and predictive substrates, identifying a trend toward generating less of the future.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.