DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation
Overview
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.
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Environment: simulation
“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,”
Exact source · revision 2Source owner not reported
Control: teleoperated
“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,”
Exact source · revision 2Source owner not reported
Reported quantity · tasks: 10 other · Basis not reported
“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”
Exact source · revision 2Source owner not reported
Reported quantity · stage-level tasks: 30 other · Basis not reported
“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”
Exact source · revision 2Source owner not reported
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- arXiv Robotics — research abstractsSignalDexArena: 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.
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