EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents
Researchers introduce EmbodiedSkills, a framework treating VLA skill decisions as verified execution proposals, reaching 86.2% success on RoboTwin 2.0.
The EmbodiedSkills framework treats each vision-language-action skill decision as an execution proposal, checking prerequisites before execution and verifying outcomes afterward via a shared executable-skill interface. It connects high-level skill selection, bounded low-level VLA execution and post-action verification in a single agent loop, and logs structured trajectories for supervision and optional online adaptation. Instantiated with Qwen3-VL and OpenPI/pi0.5, task-adapted policies achieve 86.20% average success across 50 RoboTwin 2.0 tasks and 97.40% across the four LIBERO suites, with 12.5% on memory-dependent RMBench tasks.
- Skill decisions verified as execution proposals in one agent loop
- Fixed interface lets VLA policies be swapped without changing the loop
- 86.20% average success on 50 RoboTwin 2.0 tasks
- 97.40% average success across four LIBERO suites
- Vendors
- Hugging Face
- Products
- RoboTwin 2.0LIBERORMBenchOpenPI
From the original article
1 min read at huggingface.co
Vision-language-action (VLA) models map visual observations and language instructions directly to robot actions, but long-horizon tasks require more than action prediction. An agent must coordinate perception, planning, execution, progress verification, and recovery as the physical state evolves.
By Wei Wang, Wenqiao Zhang, Yutong Lin · huggingface.co
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