E-TTS introduces a plug-and-play test-time scaling method for embodied tasks that unifies reasoning-action sampling with history buffers and closed-loop refinement to improve performance on manipulation benchmarks.
CollabVLA: Self-reflective vision-language- action model dreaming together with human
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AsyncVLA adds asynchronous flow matching and a confidence rater to VLA models so they can generate actions on flexible schedules and selectively refine low-confidence tokens before execution.
PhysReflect-VLA augments VLA policies with a Feasibility Operator, Action Explanation Operator, and LLM Reflection Module to improve success rates by an average of 5.4% on contact-rich multi-stage robotic tasks.
Proposes a cross-layer intellicise network architecture grounded in multiple theories to support intelligent complex systems, with reviews of enabling technologies and a case study.
citing papers explorer
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E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
E-TTS introduces a plug-and-play test-time scaling method for embodied tasks that unifies reasoning-action sampling with history buffers and closed-loop refinement to improve performance on manipulation benchmarks.
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AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models
AsyncVLA adds asynchronous flow matching and a confidence rater to VLA models so they can generate actions on flexible schedules and selectively refine low-confidence tokens before execution.
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PhysReflect-VLA: Physical Feasibility and Self-Reflective Regulation for Reliable Vision-Language-Action Policies
PhysReflect-VLA augments VLA policies with a Feasibility Operator, Action Explanation Operator, and LLM Reflection Module to improve success rates by an average of 5.4% on contact-rich multi-stage robotic tasks.