Seeing What Execution Needs: Benchmarking Task-Conditioned Actionable Spatial Belief for Manipulation
Seeing What Execution Needs: Benchmarking Task-Conditioned Actionable Spatial Belief for Manipulation
This research presents a benchmark called Seeing What Execution Needs, designed to evaluate how well robots can determine which visual observations are necessary for specific manipulation tasks. The study includes 1,080 frozen cases across three domains: coffee workstation, drawer/cabinet, and microwave/appliance. It evaluates various methods using over 115,000 episodes, showing that models achieve high success rates (98.8% closed-loop and 98.9% functional-OOD) when properly configured, but performance drops significantly without functional fields or command binding. The benchmark also assesses vision-language models under a full-preview contract, with results ranging from 26% to 43%. Additionally, the study explores the portability of the benchmark interface under frozen domain-specific schemas and highlights the importance of image content in maintaining high success rates.
The paper introduces a benchmark for evaluating task-conditioned actionable spatial belief in simulated tabletop manipulation, demonstrating high success rates when metadata-assisted diagnostics are used and significant performance drops when functional fields or command binding are removed.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S2
Reported numbers
evaluator episodes
>115,000
Reported trials
View original evidence
h for coffee workstation, drawer/cabinet, and microwave/appliance). The primary coffee protocols -- closed-loop actionability and same-object functional out-of-distribution generalization -- produce over 115,000 evaluator episodes across evaluated methods, budgets, and inference repeats. Each case pairs candidate RGB views with a manipulation command, shared scripted skills, and hidden postconditions, directly measur
Open source S3frozen cases
1,080
View original evidence
arXiv:2611.09794v2 Announce Type: replace Abstract: When should a robot look at the cup, and when at the portafilter? A view that is informative for one task can be useless for another, even at the same workstation. We introduce Seeing What Execution Needs, a benchmark for task-conditioned actionable spatial belief in simulated tabletop manipulation. Its aligned three-domain track contains 1,080 frozen cases (360 eac
Open source S2
- paper: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
arXiv:2611.09794v2 Announce Type: replace Abstract: When should a robot look at the cup, and when at the portafilter? A view that is informative for one task can be useless for another, even at the same workstation. We introduce Seeing What Execution Needs, a benchmark for task-conditioned actionable spatial belief in simulated tabletop manipulation. Its aligned three-domain track contains 1,080 frozen cases (360 eac
Open source S2
h for coffee workstation, drawer/cabinet, and microwave/appliance). The primary coffee protocols -- closed-loop actionability and same-object functional out-of-distribution generalization -- produce over 115,000 evaluator episodes across evaluated methods, budgets, and inference repeats. Each case pairs candidate RGB views with a manipulation command, shared scripted skills, and hidden postconditions, directly measur
Open source S3
ing whether selected observations support execution. A metadata-assisted diagnostic reference pipeline maintains spatial-functional beliefs and ranks views by missing evidence. At primary budget settings, it reaches 98.8% closed-loop and 98.9% functional-OOD success; removing functional fields or command binding reduces functional-OOD success to 0.0% and 19.7%, respectively. Under a separate full-preview contract, se
Open source S4
ven vision-language models reach 26-43%. Replacing coverage metadata with frozen RGB-derived scores retains 98.5%/98.9% success; shuffling these scores reduces success to 15%/11%, supporting dependence on image content. Separately, the aligned three-domain evaluation at H=3 supports portability of the benchmark interface under frozen domain-specific schemas. These results suggest that spatial belief for manipulation
Open source S5
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Confirm the required data type and collection setting with the buyer; this source does not establish a complete collection specification.
- Compare the reported units and scope before using these quantities in a budget. Recording hours, sensor-hours and trajectories are different measures.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：arXiv Robotics — research abstracts · arxiv.org