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Anticipation-VLA: Solving Long-Horizon Embodied Tasks via Anticipation-based Subgoal Generation

Authors
Zhilong Zhang, Wenyu Luo, Haonan Wang, Yifei Sheng, Yidi Wang, Hanyuan Guo, et al.
Source
arXiv
Published
Collection
Embodied AI
Vision Language ActionGeneralist Robot PolicyLong Horizon PlanningSubgoal GenerationRobot Manipulation
Open arXivMarkdown source

Summary

Anticipation-VLA adds adaptive subgoal generation to a goal-conditioned VLA policy for long-horizon robot tasks. It improves simulated manipulation results and reports larger gains in real-world unseen settings.

Problem

  • Standard VLA policies often fail on long-horizon tasks because small action errors compound across many steps.
  • Fixed subtask decompositions can be too coarse or too fine for the current state, so they may fail to guide execution when the robot stalls or the scene changes.
  • This matters for household and mobile manipulation tasks where the robot must complete several dependent stages under language or image goals.

Approach

  • The system keeps a stack of active goals and subgoals. A high-level anticipation model proposes the next reachable subgoal, and a low-level VLA policy acts toward it.
  • The anticipation model first generates a text subgoal, then predicts a matching subgoal image. An inverse dynamics check rejects image subgoals whose inferred instruction does not match the text subgoal.
  • A value model classifies progress as goal achieved, progress improved, or progress stalled. This classification triggers goal popping, continued execution, or recursive subgoal refinement.
  • The authors implement the high-level model with Bagel, a unified multimodal model, and implement the low-level policy by finetuning a goal-conditioned π0.5\pi_{0.5}-style flow-matching VLA.
  • Training uses hierarchical datasets for anticipation, value classification, and goal-conditioned action prediction.

Results

  • On Libero one-trajectory SFT, Anticipation-VLA reaches 80.8 average success, compared with 76.8 for π0.5\pi_{0.5}, 76.0 for π0.5\pi_{0.5}+VLM, 64.6 for π0\pi_0, 27.3 for DreamVLA, and 21.5 for UniVLA.
  • On Libero-Long, the main long-horizon suite, Anticipation-VLA scores 63.2, compared with 54.6 for π0.5\pi_{0.5} and 53.2 for π0.5\pi_{0.5}+VLM.
  • On VLABench Hammer Nail & Hang Picture, Anticipation-VLA reaches 56.3 process reward and 4.2 success rate, compared with 47.9 and 2.1 for π0.5\pi_{0.5}+VLM.
  • In real-world tests on Arx-X5, the paper uses 100 demonstrations for Rearrange Objects and 200 for Spell Words, with 40 rollouts per task split into 20 seen and 20 unseen configurations.
  • The excerpt gives no exact real-world success rates, but reports +60% gains in seen configurations and +107% in unseen configurations over the baselines. It also says Anticipation-VLA is the only tested model with non-zero success on unseen Spell Words.

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