Evo-DexBench: Benchmarking Generalization across Related Tasks for Dexterous Manipulation
General-purpose dexterous manipulation requires policies to transfer knowledge from related tasks to new tasks with only a few target demonstrations.
ProofPaper ↗
Key points
- Core tasks are related tasks that share a functional interaction and provide source knowledge, while a Gen task is a novel target within the same functional family that has only a few demonstrations.
- This setup tests whether a policy can reuse functional interaction knowledge.
- Based on this principle, we propose Evo-DexBench, which controls both source-task relatedness and target-task exposure.
- We release the Evo-DexBench dataset and benchmark to support the evaluation of dexterous manipulation policies' generalization across related tasks.
Sources (1)
- [1]Evo-DexBench: Benchmarking Generalization across Related Tasks for Dexterous ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 03:36 AM
General-purpose dexterous manipulation requires policies to transfer knowledge from related tasks to new tasks with only a few target demonstrations.
Core tasks are related tasks that share a functional interaction and provide source knowledge, while a Gen task is a novel target within the same functional family that has only a few demonstrations.
Extractive summary: sentences quoted from the sources.
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