Vision–Language–Action (VLA) policies remain brittle under modest distribution shift. On LIBERO-Plus, contemporary models that solve clean tasks at high rates can fall below 30% success when the camera's viewpoint or background changes. This paper proposes a training-free method to improve out-of-distribution (OOD) robustness for VLA policies.

Embodied Tree of Thoughts: Deliberate Manipulation Planning With Embodied World Model

MemoAct: Atkinson–Shiffrin-Inspired Hierarchical Memory-Augmented Policy for Robotic Manipulation

OmniVTLA: Vision-Tactile-Language-Action Models With Semantic-Aligned Tactile Sensing

Introduction Learning robust visuomotor policies for bimanual manipulation remains challenging due to the stringent requirements for precise coordination between arms and the ability to generalize across diverse environmental conditions. Existing approaches struggle with generalization across diverse environmental conditions for long-horizon bimanual manipulation tasks.

Long-horizon robotic manipulation requires a policy to bridge task-level semantic reasoning with metric three-dimensional interaction geometry. Existing vision–language–action policies usually acquire geometry implicitly from visual tokens or introduce deterministic intermediate representations that lack spatial grounding. This paper proposes SIRModel, which learns a spatial intermediate representation to parameter-efficiently fine-tune a vision language model for robotic manipulation tasks.

Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-action (VLA) models make each such decision with a fixed compute budget. τ0-VLA introduces world-model-guided test-time computation to a hierarchical VLA, enabling more deliberate planning for long-horizon tasks.

Introduces dynamic Roll/Stop decision-making in world action models that adapts imagination based on planning benefit, risk, and compute cost for more efficient robot planning. Enables world action models to selectively engage imagination only when beneficial.

Learning sensorimotor contingencies—that is, the link between one's actions and their sensory effects—is fundamental to developing body knowledge, understanding causality, and developing a sense of agency. In developmental psychology, this...

High-voltage grid maintenance and chemical processing demand dexterous manipulation in confined, visually occluded environments. Existing teleoperation systems present a severe cost–fidelity trade-off: industrial platforms exceed USD 60,000 yet lack spatial force feedback. STRP addresses this with a low-cost platform featuring spatial force feedback.