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Robot Manipulation Generalization On Gembench
評価指標
Average Success Rate
Average Success Rate (L1)
Average Success Rate (L2)
Average Success Rate (L3)
Average Success Rate (L4)
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
| Paper Title | ||||||
|---|---|---|---|---|---|---|
| 3D-LOTUS++ | 48.0 | 68.7±0.6 | 64.5±0.9 | 41.5±1.8 | 17.4±0.4 | Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy |
| 3D-LOTUS | 45.7 | 94.3±1.4 | 49.9±2.2 | 38.1±1.1 | 0.3±0.3 | Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy |
| 3D diffuser actor | 44.0 | 91.9±0.8 | 43.4±2.8 | 37.0±2.2 | 0.0±0.0 | 3D Diffuser Actor: Policy Diffusion with 3D Scene Representations |
| RVT-2 | 44.0 | 89.1±0.8 | 51.0±2.3 | 36.0±2.2 | 0.0±0.0 | RVT-2: Learning Precise Manipulation from Few Demonstrations |
| PolarNet | 38.4 | 77.7±0.9 | 37.1±1.4 | 38.5±1.7 | 0.1±0.2 | PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation |
| Hiveformer | 30.4 | 60.3±1.5 | 26.1±1.4 | 35.1±1.7 | 0.0±0.0 | Instruction-driven history-aware policies for robotic manipulations |
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