Experience-Guided Initiation Search for Learned Skills in Skill Composition
We propose EVIS, an Experience-Guided and Behavior-Validated Initiation Search framework for discovering reliable initiation configurations under limited target interaction.
Key points
- Deploying frozen learned skills, such as Vision-Language-Action (VLA) policies, in new environments requires identifying initiation configurations that support reliable execution.
- In skill composition, an initiation configuration affects not only the current skill but also the physical state passed to subsequent skills, so successful execution of an individual skill does not necessarily imply successful completion of the composed task.
- We evaluate EVIS on single-skill and two-stage manipulation tasks with frozen VLA policies.
- These results show that combining historical guidance with target-side behavioral validation can reduce the interaction cost of deploying frozen learned skills in new environments.
Sources (1)
- [1]Experience-Guided Initiation Search for Learned Skills in Skill CompositionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 07:46 AM
We propose EVIS, an Experience-Guided and Behavior-Validated Initiation Search framework for discovering reliable initiation configurations under limited target interaction.
Deploying frozen learned skills, such as Vision-Language-Action (VLA) policies, in new environments requires identifying initiation configurations that support reliable execution.
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