TableVerse: A Large-scale Tabletop Dataset with Real-world Grounded Layouts for Generalizable Manipulation
The development of generalizable robotic manipulation policies is inherently bounded by the availability of large-scale, high-fidelity scene data. While recent automated synthesis methods attempt to bridge this gap via text-to-layout hallucination or simplified procedural generation, they frequently suffer from physical implausibility and fail to capture the complex, dense clutter of actual human environments. In this paper, we introduce TableVerse, a fully automated Real2Sim pipeline that shift
Record details
Published: 22 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 25 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions
HuggingFace Daily Papers · 22 July 2026
Sample-Efficient Learning from Agent Experience
HuggingFace Daily Papers · 22 July 2026
OpenForgeRL: Train Harness-native Agents in Any Environment
HuggingFace Daily Papers · 22 July 2026
Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning
arXiv cs.HC · 22 July 2026
Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids
arXiv · 22 July 2026
Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments
arXiv cs.AI · 22 July 2026
How to cite this record
ethics.ai (22 July 2026), “TableVerse: A Large-scale Tabletop Dataset with Real-world Grounded Layouts for Generalizable Manipulation,” evidence record 13022, https://ethics.ai/record/13022 (originally published by HuggingFace Daily Papers).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.