ABot-M0.5 proposes a unified mobility-and-manipulation world action model using three alignment strategies that achieves state-of-the-art performance on mobile and fine-grained manipulation benchmarks.
Saivla-0: Cerebrum–pons–cerebellum tripartite architecture for compute-aware vision-language-action.arXiv preprint arXiv:2603.08124, 2026
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RDGen uses sim-to-real RL policies to generate smoother robot demonstrations that improve downstream VLA performance over human-collected data on pick-and-place tasks.
Evo-Depth is a compact VLA model using a lightweight implicit depth encoder from RGB views plus progressive alignment to boost manipulation performance without added hardware.
citing papers explorer
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ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
ABot-M0.5 proposes a unified mobility-and-manipulation world action model using three alignment strategies that achieves state-of-the-art performance on mobile and fine-grained manipulation benchmarks.
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RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning
RDGen uses sim-to-real RL policies to generate smoother robot demonstrations that improve downstream VLA performance over human-collected data on pick-and-place tasks.
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Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model
Evo-Depth is a compact VLA model using a lightweight implicit depth encoder from RGB views plus progressive alignment to boost manipulation performance without added hardware.