Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
One-Shot Imitation Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train with behavioral cloning instead of DAGGER, using noise-injected trajectories (ablation) | One-Shot Imitator · Block stacking tasks | success rate | No clear effect |
| Condition on the full demonstration instead of only the final state (ablation) | One-Shot Imitator · Block stacking tasks with more stages | success rate | Helped |
| Condition on the full demonstration instead of a snapshot containing the last frame of each stage (ablation) | One-Shot Imitator · Block stacking tasks | success rate | Helped |
| Use soft attention over the demonstration and current state | One-Shot Imitator · Novel block configurations | success rate | Helped |
Closest papers by meaning
- Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control (NeurIPS 2024 · similarity 0.91)
- Instant Policy: In-Context Imitation Learning via Graph Diffusion (ICLR 2025 · similarity 0.89)
- Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration (ICML 2024 · similarity 0.89)
- What's the Move? Hybrid Imitation Learning via Salient Points (ICLR 2025 · similarity 0.89)
- Imitation Learning from Observation with Automatic Discount Scheduling (ICLR 2024 · similarity 0.89)
- OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation (NeurIPS 2025 · similarity 0.89)
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