ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning
Learn how ESPADA speeds up imitation learning by downsampling demonstration data using semantics-aware methods, improving execution speed without sacrificing accuracy
- Apply ESPADA framework to segment demonstration data using VLM-L
- Downsample demonstration data using semantics-aware methods to reduce dataset size
- Train visuomotor policies using the downsampled data to improve execution speed
- Evaluate the performance of the trained policies using metrics such as accuracy and speed
- Fine-tune the ESPADA framework to adapt to diverse manipulation settings and tasks
Researchers and engineers working on imitation learning and visuomotor policies can benefit from ESPADA to improve the efficiency of their models, especially in robotics and computer vision applications
💡 ESPADA's semantics-aware downsampling approach can significantly improve the execution speed of visuomotor policies while maintaining accuracy, making it a valuable tool for robotics and computer vision applications
💡 Speed up imitation learning with ESPADA! This framework downsamples demo data using semantics-aware methods, improving execution speed without sacrificing accuracy #ImitationLearning #VisuomotorPolicies
Key Takeaways
Learn how ESPADA speeds up imitation learning by downsampling demonstration data using semantics-aware methods, improving execution speed without sacrificing accuracy
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Abstract:
arXiv:2512.07371v3 Announce Type: replace-cross Abstract: Behavior-cloning based visuomotor policies enable precise manipulation but often inherit the slow, cautious tempo of human demonstrations, limiting practical deployment. However, prior studies on acceleration methods mainly rely on statistical or heuristic cues that ignore task semantics and can fail across diverse manipulation settings. We present ESPADA, a semantic and spatially aware framework that segments demonstrations using a VLM-L
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