SWAM studies how action learning affects language-video alignment
Authors report 88.10% easy and 61.56% hard RoboTwin success, and 40.00% versus 23.33% on three real tasks.
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Stories tagged #robotwin
Just Action Models match world-action policies without visual generation
Against FastWAM, reported LIBERO success moves from 97.0%, 55.3%, and 91.2% to 98.2%, 64.5%, and 91.4%.
FocusVLA routes multi-view inputs using predicted future intent
On RoboTwin 2.0 it raises average success 10.10 points over π0.5, and real-world success from 71.7% to 85.0%.
VLA-Δ: Learning Physical State Transition for Vision-Language-Action Models
VLA-Δ trains robot policies on physical state transitions and beats π0.5 in real-robot tests.
RoboRSI: Stable, efficient, and reusable robot self-evolution in complex real-world environments
RoboRSI lets a mobile manipulator reuse validated skills, beating baselines by up to 11 points.
DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication
DuoMind coordinates multiple robots using VLM orchestrators, VLA action models, and semantic messages.
World Action Modeling with Progressive Visual Planning
ProWAM predicts actions and sparse visual sub-goals, posting new robotics scores and 70% real-world success.
Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation
ViGAR plans visual subgoals for robot manipulation, beating the best RoboTwin baseline by 12.86 points.
EVO-WAM: Evolving World Action Models through Video-Action Verification
EVO-WAM self-improves world action models on unseen robot tasks without new expert demonstrations.
AnyStep-WAM: Budget-Aligned Distillation and Adaptive Inference for World Action Models
AnyStep-WAM cuts world-action denoising steps by up to 85% while holding RoboTwin success rates.
Rolling-WAM: World Action Models with Rolling Imagination
Rolling-WAM speeds robotic replanning 4.5x by spreading joint denoising across successive cycles.
Rolling-WAM: World Action Models with Rolling Imagination
Rolling-WAM speeds robot replanning 4.5x by staggering joint video-action denoising across cycles.
DeltaWAM: Delta World Action Models for Bimanual Manipulation
DeltaWAM predicts visual deltas and robot actions, raising RoboTwin success while cutting training and inference cost.
Transferring the Intelligence of VLMs to Robotic Control
RoboDawn lets a VLM control robots via discrete commands, beating trained policies zero-shot and one-shot.