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Robot Manipulation Generalization On Gembench
Metrics
Average Success Rate
Average Success Rate (L1)
Average Success Rate (L2)
Average Success Rate (L3)
Average Success Rate (L4)
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||||||
|---|---|---|---|---|---|---|---|
| 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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