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#Robot foundation models (6)

WaterfallPagination
Facet-0: a contact-aware foundation model for precise assembly
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Facet-0: a contact-aware foundation model for precise assembly

@robotsdigest · 207 views · 2026-09-02
VLAManuFacet-1KFacet-0
π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

π0.7 is a steerable generalist robotic foundation model conditioned on diverse multimodal context—language commands, subgoal images, and task metadata—enabling strong out-of-the-box performance in unseen environments, zero-shot cross-embodiment transfer, and emergent capabilities matching RL-finetuned specialists.

Physical Intelligence, Bo Ai, Ali AminApr 16, 2026
Robot foundation modelsVLAvision-language-actionApr 16, 2026
Dyna Crosses the Customer ROI Threshold: Dyna-2 Reaches 1,590 Napkins Per Day While Deployment Becomes the Eval

Dyna Crosses the Customer ROI Threshold: Dyna-2 Reaches 1,590 Napkins Per Day While Deployment Becomes the Eval

Dyna says Din Tai Fung will scale its robots after crossing the customer ROI threshold. Dyna-2 raises napkin folding from 35 to 95 per hour with quality improving from 75% to 93%, reaching 1,590 table-ready napkins daily. The essay details the production stack: reliable episode capture, automatic SOP labeling, failure drill-downs, hardware degradation prediction, and a deployment flywheel that can reach ROI in three days.

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Dyna-2Din Tai FungRobot foundation modelsCommercial DeploymentProduction Evaluation
Introducing S1: In-Context Learning for Robotics — One Video Demonstration Is All It Takes

Introducing S1: In-Context Learning for Robotics — One Video Demonstration Is All It Takes

Skild AI's flagship robotic foundation model S1 is built from the ground up as an in-context learner: show it a video demonstration of a task — even an unseen 10-minute long-horizon one — and it executes with no fine-tuning. On unseen tasks, one demonstration ≈ 380 post-training examples; at 100k pre-training hours S1 reaches 66% success vs 9% for language-prompted VLAs.

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Skild AIS1in-context learningVLAContext Learning
Flex-π: compute-flexible robot foundation model
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Flex-π: compute-flexible robot foundation model

@yokoshaan · 78 views · 2026-08-14
VLAFlex-πRobot foundation models
Enigma puts 100+ robots under unscripted internet user commands
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Enigma puts 100+ robots under unscripted internet user commands

@roboindexio · 75 views · 2026-08-12
Zero-ShotEnigmaRobot foundation models