DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation
DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation
This research presents DexArena, a unified benchmark designed to evaluate long-horizon bimanual dexterous manipulation. The benchmark includes 10 long-horizon tasks and 30 stage-level tasks across diverse household and industrial scenarios. Each task features explicitly defined stages with physically grounded success conditions, allowing for reliable evaluation of intermediate progress and end-to-end completion. A unified interface supports testing various policy learning algorithms using standardized simulation configurations, scene randomization, trajectory replay, and task-level evaluation. Extensive baseline experiments highlight challenges in precise manipulation, generalization to scene variations, and skill composition, establishing a reproducible framework for advancing robot learning methods toward generalizable long-horizon dexterous behavior.
The paper introduces DexArena, a new benchmark comprising 10 long-horizon tasks and 30 stage-level tasks to evaluate bimanual dexterous manipulation under standardized simulation conditions with verified human-teleoperated demonstrations.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S4
- Control
- TeleoperatedOpen source S4
Reported numbers
tasks
10
View original evidence
abilities difficult to evaluate systematically. We introduce DexArena, a benchmark designed to evaluate learning and generalization in long-horizon bimanual dexterous manipulation. DexArena features 10 long-horizon tasks and 30 stage-level tasks spanning diverse household and industrial scenarios. Each long-horizon task is composed of explicitly defined stages with physically grounded success conditions, enabling rel
Open source S3stage-level tasks
30
View original evidence
abilities difficult to evaluate systematically. We introduce DexArena, a benchmark designed to evaluate learning and generalization in long-horizon bimanual dexterous manipulation. DexArena features 10 long-horizon tasks and 30 stage-level tasks spanning diverse household and industrial scenarios. Each long-horizon task is composed of explicitly defined stages with physically grounded success conditions, enabling rel
Open source S3
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
iable evaluation of both intermediate progress and end-to-end task completion. A unified benchmark interface and verified human-teleoperated demonstrations enable testing different policy learning algorithms, with standardized simulation configurations, scene randomization, trajectory replay, and task-level evaluation. Through extensive baseline experiments, we identify substantial challenges in precise manipulation,
Open source S4
abilities difficult to evaluate systematically. We introduce DexArena, a benchmark designed to evaluate learning and generalization in long-horizon bimanual dexterous manipulation. DexArena features 10 long-horizon tasks and 30 stage-level tasks spanning diverse household and industrial scenarios. Each long-horizon task is composed of explicitly defined stages with physically grounded success conditions, enabling rel
Open source S3
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Confirm the required data type and collection setting with the buyer; this source does not establish a complete collection specification.
- Compare the reported units and scope before using these quantities in a budget. Recording hours, sensor-hours and trajectories are different measures.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：arXiv Robotics — research abstracts · arxiv.org