MuseVLA adds on-demand sensor selection via tokens and converts readings into grounded sensor images for multimodal fusion, reporting 80.6% average success on real-robot dexterous tasks that need non-visual sensing.
Vlas: Vision-language-action model with speech instructions for customized robot manipulation
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Sparse retry keypoints plus pairwise preference learning yield mistake-sensitive values that reweight mixed-quality demos and raise real-robot imitation success over progress-based baselines.
By inverting a frozen flow-matching robot policy with fixed-point iteration, UniSteer converts human corrections into noise-space supervision and lifts real-world VLA adaptation success from 20% to 90% in about 66 minutes on average.
A discrete diffusion model tokenizes multimodal robotic data and uses a progress token to predict future states and task completion for scalable policy evaluation.
ThermoAct integrates thermal imaging into VLA models via a VLM planner to enable robots to perceive physical properties like heat and improve safety over vision-only systems.
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.
citing papers explorer
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MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation
MuseVLA adds on-demand sensor selection via tokens and converts readings into grounded sensor images for multimodal fusion, reporting 80.6% average success on real-robot dexterous tasks that need non-visual sensing.
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Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation
Sparse retry keypoints plus pairwise preference learning yield mistake-sensitive values that reweight mixed-quality demos and raise real-robot imitation success over progress-based baselines.
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UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
By inverting a frozen flow-matching robot policy with fixed-point iteration, UniSteer converts human corrections into noise-space supervision and lifts real-world VLA adaptation success from 20% to 90% in about 66 minutes on average.
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dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model
A discrete diffusion model tokenizes multimodal robotic data and uses a progress token to predict future states and task completion for scalable policy evaluation.
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ThermoAct:Thermal-Aware Vision-Language-Action Models for Robotic Perception and Decision-Making
ThermoAct integrates thermal imaging into VLA models via a VLM planner to enable robots to perceive physical properties like heat and improve safety over vision-only systems.
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PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
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DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.