Source note
V-CAGE: Vision-Closed-Loop Agentic Generation Engine for Robotic Manipulation
Summary
V-CAGE is a data generation pipeline for robotic manipulation that builds simulation scenes, executes tasks, checks them with a vision-language model, and compresses the resulting videos. The paper targets long-horizon Vision-Language-Action training, where bad scene layouts and undetected execution errors can poison large synthetic datasets.
Problem
- VLA models need large manipulation datasets, but real data collection is expensive and misses many long-tail cases.
- Existing synthetic pipelines often place objects without enough task context, which causes collisions, occlusion, or unreachable targets.
- Many systems run in open loop: code finishes without runtime errors, but the task can still fail visually, and those silent failures contaminate long-horizon training data.
Approach
- V-CAGE uses an agentic pipeline built on OpenClaw to turn a language task into a usable simulation scene and verified manipulation trajectory.
- Its Inpainting-Guided Scene Construction first selects relevant assets, places them without collisions, writes a semantic layout plan, then uses image inpainting to rearrange objects into a task-aware scene.
- It recovers object positions from the edited image with Grounding DINO and DINOv2 matching, then refines coordinates with constrained optimization to remove collisions while keeping the intended layout.
- It searches for executable subtasks from predefined manipulation templates using object metadata such as grasp points and functional points, instead of generating all robot code from scratch.
- After execution, Gemini 3 checks each subtask from visual observations; any failed step causes the whole trajectory to be rejected. The pipeline also compresses videos with action-aware keyframe selection and HEVC CRF tuning under a 0.1 JOD perceptual loss threshold.
Results
- For long-horizon policy learning, the paper fine-tunes a π0.5 VLA model on 4 tasks with 100 synthetic expert trajectories per task and evaluates over 100 trials per task.
- Zero-shot pre-train success is 0% on all 4 tasks. After training on raw synthetic data, success rates are 54% on AutoCheckout, 54% on PackBreads, 100% on PackStationery, and 25% on SortToCabinet.
- Training on compressed data gives similar results: 52%, 50%, 100%, and 28% on the same tasks, which supports the claim that compression preserves training utility.
- In Sim2Real on ALOHA-AgileX, using 10 real demos alone gives 20% success over 20 trials. Co-training with 10 real + 250 simulated trajectories raises success to 55%, an absolute gain of 35 points.
- The compression method claims over 90% reduction in file size; Figure 1 and the method section report about 93% while keeping perceptual loss below 0.1 JOD.
- The excerpt does not provide standalone ablation numbers for IGSC or VLM verification beyond these end-task results, though the paper claims they improve scene feasibility and remove silent-failure trajectories.