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StarVLA-α: Reducing Complexity in Vision-Language-Action Systems
This work introduces StarVLA-$\alpha, a simple yet strong baseline designed to study VLA design choices under controlled conditions that deliberately minimizes architectural and pipeline complexity to reduce experimental confounders and enable systematic analysis.
What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
This work characterize models from this family of methods as JEPA-WMs and investigates the technical choices that make algorithms from this class work, and proposes a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks.
Show-Harness: Just a VLM Agent Can Play Robots
Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to"play"robots through a compact semantic interface linking intent to action. Show-Harness exposes...
When to Trust Imagination: Adaptive Action Execution for World Action Models
Future Forward Dynamics Causal Attention (FFDC), a lightweight verifier that reasons over predicted actions, predicted visual dynamics, real observations, and instruction-conditioned semantics, is proposed that allows continued execution when a plan remains reliable and triggers replanning when its validity deteriorate...