arXiv:2607. 14439v1 Announce Type: new Abstract: Generalist robot manipulation policies trained on large, diverse datasets have shown remarkable promise across a wide range of tasks.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2604. 09860v4 Announce Type: replace-cross Abstract: The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid performance saturation and a lack of true generalization testing.
arXiv:2606. 08881v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong generalization in robotic manipulation, yet existing evaluations are primarily conducted in simulation or on expensive robotic platforms, leaving their robustness on affordable real-world robots largely unexplored.
arXiv:2607. 04434v1 Announce Type: cross Abstract: Generalist robot manipulation policies have advanced rapidly, yet existing benchmarks remain limited in systematically evaluating their capabilities.
arXiv:2603. 22876v2 Announce Type: replace-cross Abstract: Learning a generalist control policy for robotic manipulation typically relies on large-scale datasets.
arXiv:2510. 17640v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong manipulation capability when trained with large-scale imitation learning datasets.
arXiv:2606. 18247v1 Announce Type: cross Abstract: Robots deployed in the real world should learn from their experience and improve over time.