Get Started
Home
Topics
Search
Library
Research questionHow can we diagnose vision-language-action models’ failures on spatially ambiguous, long-horizon manipulation tasks?Many VLA evaluations report whether a manipulation task succeeds, but that outcome does not reveal whether failures arise from spatial interpretation, precise execution, or maintaining a multi-step plan. Increasing ambiguity and procedural length make these failure modes harder to distinguish across scenes and embodiments.
Evaluation & Benchmarks
Multimodal Models
Reasoning
Robotics
Latest papersRecent research connected to this question, newest first.RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?RoboSPA evaluates VLA models across fine-grained spatial reasoning and long-horizon procedural planning, with 10 task categories, 56 base tasks, five difficulty levels, and 280 task variants. It includes 527K trajectories across multiple embodiments and diverse scenes, and reports diagnostic measures beyond binary success; experiments on representative models show difficulties with complex spatial relations, low-level execution, and memory-intensive planning.research paper · Sep 4, 2026
Related questions
How can vision-language-action systems reliably execute long-horizon manipulation while tracking state and conditional dependencies?How can vision-language-action robots be evaluated for execution quality and decision confidence beyond binary task success?How can pretrained vision-language-action models reliably perform contact-rich manipulation when goals, scenes, and contacts change?How can robotic vision-language-action models generalize across backbones without losing hierarchical manipulation structure?