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Meta-World

Benchmarks

Task NameDataset NameSOTA ResultTrend
Open doorMeta-World
VOC Score62.64
35
Reward ModelingMeta-World Open door
Prediction Accuracy65.46
28
Reward ModelingMeta-World Open drawer
Prediction Accuracy69.01
28
Reward ModelingMeta-World Button press
Prediction Accuracy76.44
28
Open drawerMeta-World
VOC Score90.17
28
Button pressMeta-World
VOC Score95.47
28
Robotic ManipulationMeta-World
Average Success Rate94.7
27
Robot ManipulationMeta-World
Latency (Easy) (ms)10.1
15
Multi-task Reinforcement LearningMeta-World MT10 v1 (Fixed)
Success Rate88
12
Offline Reinforcement LearningMeta-World medium-replay
BP->DC*3,967
10
Multi-Task Reinforcement LearningMeta-World MT10 v1 (train test)
Average Success91
9
reachMeta-World ML-1 (test)
Success Rate100
9
Multi-task Reinforcement LearningMeta-World MT50 (MT50-rand) V2 (Near-optimal)
Avg Success Rate61.32
8
Task GeneralizationMeta-World ML-45 (test)
Success Rate81.7
8
Task GeneralizationMeta-World ML-10 (test)
Success Rate97.5
8
Multi-task Reinforcement LearningMeta-World MT50 v1 (Fixed)
Success Rate60
8
door-unlockMeta-World v2 (test)
Best Attack Reward3,421
7
door-lockMeta-World v2 (test)
Best Attack Reward2,043
7
handle-pull-sideMeta-World v2 (test)
Best Attack Reward4,268
7
handle-press-sideMeta-World v2 (test)
Best Attack Reward4,726
7
faucet-openMeta-World v2 (test)
Best Attack Reward4,383
7
faucet-closeMeta-World v2 (test)
Best Attack Reward4,108
7
drawer-openMeta-World v2 (test)
Best Attack Reward1,556
7
drawer-closeMeta-World v2 (test)
Best Attack Reward4,868
7
window-openMeta-World v2 (test)
Best Attack Reward671
7
Showing 25 of 88 rows