Research map / Vision-language and multimodal / Robot action and navigation
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs
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Proposes PIVOT, a zero-shot method that turns robot control and spatial localization into visual question answering by annotating images with sampled candidate actions and iteratively refining a Gaussian proposal distribution using VLM selections.
Problem
VLMs output only text, yet robotic control and spatial tasks need continuous coordinates, actions, or trajectories; the question is how to elicit such continuous outputs zero-shot from VLMs.
Approach
Lift continuous actions into the visual input by drawing numbered candidate arrows or points on the image, ask the VLM to select promising candidates, then refit an isotropic Gaussian to the selected candidates and repeat, a cross-entropy-method-like loop with no training.
Key results
- On real mobile manipulator navigation, 3 iterations with 3 parallel calls raised success on several tasks, e.g., 'Go to the darker room' from 25% (no iteration, no parallel) to 100%.
- On real pick tasks, 3 iterations with 3 parallel calls achieved 100% reach and 67% grasp on 'Pick coke can', versus 50% reach and 0% grasp with no iteration.
- On RefCOCO testA (1000 samples), GPT-4V with PIVOT shows strong first-iteration performance with modest gains over further iterations.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add iterative refinement (ablation) | PIVOT navigation · Mobile manipulator navigation tasks | success rate: 25% -> 50%; 75% -> 50% | Mixed |
| Add 3 parallel calls (ablation) | PIVOT navigation · Mobile manipulator navigation tasks | success rate: 25% -> 75% | Helped |
| Add 3 iterations and 3 parallel calls (ablation) | PIVOT navigation · Go to the darker room | success rate: 25% -> 100% | Helped |
| Add 3 iterations (ablation) | PIVOT mobile manipulator manipulation · Pick coke can | Reach: 50% -> 67% | Helped |
| Add 3 parallel calls to 3-iteration PIVOT (ablation) | PIVOT mobile manipulator manipulation · Bring the orange to the X | Reach: 80% -> 67% | Hurt |
| Add 3 parallel calls to 3-iteration PIVOT (ablation) | PIVOT mobile manipulator manipulation · Pick coke can | Grasp: 33% -> 67% | Helped |
| Add 3 iterations and 3 parallel calls (ablation) | PIVOT Franka · Real-world Franka manipulation | YZ: 38% -> 59% | Helped |
| Use image annotations instead of text-only grid regions (ablation) | Navigation localization · Navigation offline, in-view | L2 loss: $0.26\pm0.15$ -> $0.21\pm0.002$ | Helped |
| Use image annotations instead of text-only grid regions (ablation) | Navigation localization · Navigation offline, semantic | L2 loss: $0.35\pm0.14$ -> $0.23\pm0.012$ | Helped |
| Use image annotations instead of text-only grid regions (ablation) | Navigation localization · Navigation offline, out-of-view | L2 loss: $0.46\pm0.31$ -> $0.44\pm0.04$ | Helped |
| Increase iterations from 1 to 3 and parallel calls from 0 to 3 (ablation) | PIVOT navigation · Navigation offline, in-view | L2 loss: $0.21\pm0.002$ -> $0.17\pm0.009$ | Helped |
| Increase iterations from 1 to 3 and parallel calls from 0 to 3 (ablation) | PIVOT navigation · Navigation offline, semantic | L2 loss: $0.23\pm0.012$ -> $0.19\pm0.01$ | Helped |
Surprising findings
- Increasing the number of sampled candidates improves initial answers but degrades later optimization due to occlusion and crowding.
- VLMs were unable to reliably choose actions based on depth encoded via color and label size.
Limitations
- VLMs cannot reliably reason about depth or 3D actions, and rotation is not handled.
- Occlusion in closed-loop first-person views and lack of embodied training data degrade grasping decisions.
- Greedy, myopic behavior on multi-step tasks and vision-language connection errors.
Experimental setup
Datasets: RefCOCO RT-X RAVENS
Benchmarks: RefCOCO testA subset RT-X mobile manipulator offline manipulation navigation offline dataset RAVENS real-world mobile manipulator and Franka tasks
Metrics: success rate normalized L2 distance cosine similarity grasp rate steps
Baselines: text-only action selection GPT-4V text baseline (region grid)
Builds on: cross-entropy method Set-of-Mark prompting chain-of-thought
Closest papers by meaning
- Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes (ICLR 2026 · similarity 0.91)
- CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models (CVPR 2025 · similarity 0.90)
- Spatial Understanding from Videos: Structured Prompts Meet Simulation Data (NeurIPS 2025 · similarity 0.90)
- ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models (NeurIPS 2026 · similarity 0.89)
- SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors (NeurIPS 2024 · similarity 0.89)
- Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models (ICML 2024 · similarity 0.89)
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