A systematic study of hierarchical VLA agents identifies design principles that improve robot manipulation performance over flat and naive hierarchical baselines in simulation and real-world experiments.
Benchmarking vision, language, & action models on robotic learning tasks
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
SOTA VLA models like OpenVLA and pi0 collapse when object positions are perturbed, indicating that standard LIBERO scores reward memorization; LIBERO-PRO provides a systematic perturbed evaluation suite.
VS-Bench is a new benchmark of ten visual multi-agent environments that measures VLMs on element recognition, next-action prediction, and normalized episode return, showing strong perception but large gaps in reasoning and decision-making with the best model at 46.6% prediction accuracy and 31.4% of
citing papers explorer
-
What Matters in Orchestrating Robot Policies: A Systematic Study of Hierarchical VLA Agents
A systematic study of hierarchical VLA agents identifies design principles that improve robot manipulation performance over flat and naive hierarchical baselines in simulation and real-world experiments.
-
LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
SOTA VLA models like OpenVLA and pi0 collapse when object positions are perturbed, indicating that standard LIBERO scores reward memorization; LIBERO-PRO provides a systematic perturbed evaluation suite.
-
VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments
VS-Bench is a new benchmark of ten visual multi-agent environments that measures VLMs on element recognition, next-action prediction, and normalized episode return, showing strong perception but large gaps in reasoning and decision-making with the best model at 46.6% prediction accuracy and 31.4% of