Recoleta
GitHub
Source note

PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation

Authors
Yutai Li, Shaohui Peng, Jiaming Guo, Di Huang, Zihao Zhang, Yuxuan Guo, et al.
Source
arXiv
Published
Collection
Embodied AI
Vision Language ActionRobot Foundation ModelMotion PrimitivesGeneralist Robot PolicyRobot Data Scaling
Open arXivMarkdown source

Summary

PrimitiveVLA fine-tunes VLA robot policies on reusable motion primitives cut from whole task demonstrations. The paper claims better data efficiency and stronger zero-shot transfer on LIBERO through training-time segmentation and test-time primitive sequencing.

Problem

  • Standard VLA fine-tuning maps a full language instruction directly to low-level robot actions, which encourages memorization of task-specific trajectories and visual cues.
  • This matters because each new object-skill pair or long-horizon task can require more robot demonstrations, which slows robot data scaling.
  • Public robot datasets usually provide task-level instructions only, so primitive labels and segment boundaries are missing.

Approach

  • The method defines 11 primitives: Grasp, Place, Lift, Move, Push, Pull, Insert, Press, Twist, Tilt, and Rotate.
  • During fine-tuning, a VLM infers the primitive sequence from the task instruction, RGB trajectory, and primitive library.
  • An LLM writes Python rules over robot state to detect primitive boundaries, such as gripper closure followed by upward motion for Grasp.
  • The VLA is trained with canonical primitive instructions and object-centric masks tracked with SAM and Cutie, so different tasks share the same primitive input format.
  • During inference, the system retrieves the top-3 similar task-sequence examples, uses a VLM to plan primitives, and uses LLM-generated switch code over a sliding state history while the VLA outputs continuous actions.

Results

  • On LIBERO-90, OpenVLA + PrimitiveVLA reaches 79.80% success versus 70.60% for OpenVLA, a +9.20 point gain.
  • On LIBERO-90-Novel, OpenVLA rises from 7.38% to 45.50%, about a 6.2x gain on unseen tasks.
  • OpenVLA-OFT + PrimitiveVLA reaches 71.00% on LIBERO-90-Novel versus 13.50%, and 66.50% on LIBERO-Long versus 3.75%.
  • pi0.5 + PrimitiveVLA reaches 75.50% on LIBERO-90-Novel versus 56.00%, and 80.25% on LIBERO-Long versus 30.50%.
  • Small-scale LIBERO mean improves from 82.40% to 88.00% for OpenVLA and from 97.60% to 98.53% for OpenVLA-OFT; pi0.5 changes from 97.53% to 97.33%.
  • The excerpt says simulation uses 50 trials and real-world/RLBench use 20 trials, but it does not include the real-world or RLBench result tables.

Link