RoboCasa-Shift measures stage-aware robustness of household VLA policies
GR00T N1.5 ran 1,040 rollouts; equal success drops hid different first-stage failure rates.
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Stories tagged #robocasa
Can World and Action Modeling Benefit Each Other under Unified Causal Training?
OneWM shows unified causal training lets world modeling and action learning improve each other.
Long-WAM: Scaling the Context of World-Action Models
Long-WAM raises robot task success by scaling causal world-action context with autoregressive video pretraining.
EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution
EmbodiedRSI reaches 77% success on RoboCasa365, nearly doubling the best baseline.
DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling
DiVeR reweights VLA verifier training toward decision-critical states, improving robot task success with little inference overhead.
MM-ABC: Towards Generalist Mobile Manipulation via Seeing, Coordinating and Imagining
MM-ABC coordinates arm and base control for generalist mobile manipulation in simulation and on real robots.
Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies
A 0.8B robot policy distilled from cached world-model features hits 97.9% on LIBERO without extra test-time compute.