{
  "schema": "project-telos.research-intake/embodied-sim2real-source-receipts-v1",
  "created_at": "2026-07-02T00:00:00.000Z",
  "source_boundary": {
    "metadata_only": true,
    "full_text_stored": false,
    "domain_claims_promoted": false,
    "fixture_claims_promoted": true,
    "notes": "Rows are arXiv metadata receipts and requirements pressure only. They are not treated as proof of external robotics, medical, safety, or foundation-model claims."
  },
  "query_receipts": [
    {
      "query": "robotics foundation models embodied intelligence sim to real",
      "scope": ["robotics", "foundation models", "sim-to-real", "embodied AI"],
      "kept": 5,
      "dropped": 3,
      "digest_seal": "ee058ddfeba15e3c8b329ab70c2479dd10db39058d982dd1461f96d376c7145c"
    },
    {
      "query": "vision language action models robotics benchmark",
      "scope": ["VLA", "robotics", "benchmarks", "embodied agents"],
      "kept": 3,
      "dropped": 5,
      "digest_seal": "8fb5e2c9b71e61f670775a67928213737452de02624ab43ef435cd24d7fe2015"
    },
    {
      "query": "world models robotics embodied AI control",
      "scope": ["world models", "robotics", "control", "embodied AI"],
      "kept": 8,
      "dropped": 0,
      "digest_seal": "2f2415dfc4bad1ea7787466e1e815d6c73e8273ddac33773509f695e8dc22665"
    },
    {
      "query": "surgical robotics foundation model autonomous surgery",
      "scope": ["surgical robotics", "foundation models", "medicine", "autonomy"],
      "kept": 2,
      "dropped": 6,
      "digest_seal": "7f024e64e71dbb41cf056c3b142894210c60cc695d4f373b113ec968bcb9f89b"
    },
    {
      "query": "soft robotics biology medicine embodied intelligence",
      "scope": ["soft robotics", "biology", "medicine", "embodied intelligence"],
      "kept": 3,
      "dropped": 5,
      "digest_seal": "4017b7ad014751a4904283998f253d5b00add38b0f9b32282a39d4c33293266a"
    }
  ],
  "relevant_receipts": [
    {
      "id": "2606.11324v1",
      "title": "Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models",
      "ref": "http://arxiv.org/abs/2606.11324v1",
      "sha256": "0dc03d4145608aae23b456e2f5c9a92114dea0383e0606914e9fc5733c7f32ff",
      "role": "SOURCE_LEAD_embodied_foundation_models"
    },
    {
      "id": "2505.20503v2",
      "title": "Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review",
      "ref": "http://arxiv.org/abs/2505.20503v2",
      "sha256": "816600d53cbb2ff5290ee34298f462e779f8e81574b954e228a54bad1b1c87f9",
      "role": "SOURCE_LEAD_mobile_service_robot_survey"
    },
    {
      "id": "2507.00917v3",
      "title": "A Survey: Learning Embodied Intelligence from Physical Simulators and World Models",
      "ref": "http://arxiv.org/abs/2507.00917v3",
      "sha256": "18f45c66cca25927b4521fbd47f6a5f45fc217af1f38ed18045d6cf7cab0acfb",
      "role": "SOURCE_LEAD_simulators_world_models"
    },
    {
      "id": "2407.06886v8",
      "title": "Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI",
      "ref": "http://arxiv.org/abs/2407.06886v8",
      "sha256": "f73b76988ea51e2bbeb888d5e896f5b8c1b866c0a77a19b7aedbbc4f2d4b722e",
      "role": "SOURCE_LEAD_embodied_ai_survey"
    },
    {
      "id": "2605.02900v2",
      "title": "Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses",
      "ref": "http://arxiv.org/abs/2605.02900v2",
      "sha256": "b40ca55cfefbd71e390c7c946e958f3dbcd74ac238476e02f3d40d084ff207af",
      "role": "SOURCE_LEAD_embodied_ai_safety"
    },
    {
      "id": "2307.15818v1",
      "title": "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control",
      "ref": "http://arxiv.org/abs/2307.15818v1",
      "sha256": "3f251bcb180b7707d03ef5103c8613acc59679e78eeea609d2dac19bbf4b1d75",
      "role": "SOURCE_LEAD_vla_robot_control"
    },
    {
      "id": "2503.20020v1",
      "title": "Gemini Robotics: Bringing AI into the Physical World",
      "ref": "http://arxiv.org/abs/2503.20020v1",
      "sha256": "2aba06b50f438f8dfce31a1858e364373b1f4b355e71cf3a95a1a2dcec93a9a6",
      "role": "SOURCE_LEAD_physical_world_robotics_model"
    },
    {
      "id": "2304.08743v2",
      "title": "Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints",
      "ref": "http://arxiv.org/abs/2304.08743v2",
      "sha256": "4b0f54dcecfd9e04a3d7937768cfe2c16329de68fd82c80458334360adaeb1f1",
      "role": "SOURCE_LEAD_action_constraints"
    },
    {
      "id": "2409.16828v3",
      "title": "On the role of Artificial Intelligence methods in modern force-controlled manufacturing robotic tasks",
      "ref": "http://arxiv.org/abs/2409.16828v3",
      "sha256": "b87f6905295cfad2edcf5f96f62f19c6b628a93cd102ed246eb7d7df99ebab75",
      "role": "SOURCE_LEAD_force_control_manufacturing"
    },
    {
      "id": "2109.07120v3",
      "title": "Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments",
      "ref": "http://arxiv.org/abs/2109.07120v3",
      "sha256": "3b4ae30c3c44a6bca14222fc70e8a991a133138ad4e16b9b7000d66319691261",
      "role": "SOURCE_LEAD_mpc_meta_rl"
    },
    {
      "id": "1906.04852v1",
      "title": "Snake-Like Robots for Minimally Invasive, Single Port, and Intraluminal Surgeries",
      "ref": "http://arxiv.org/abs/1906.04852v1",
      "sha256": "3de6005d6d5988f08e47d16c1d47caa9ca778c29eb215f7ff3f44a1b7c439d6e",
      "role": "SOURCE_LEAD_surgical_robotics"
    },
    {
      "id": "2406.09990v1",
      "title": "Task segmentation based on transition state clustering for surgical robot assistance",
      "ref": "http://arxiv.org/abs/2406.09990v1",
      "sha256": "67c9b5f77ae30cfe58e88858e33b0ce058739a2c7d747057c03194b187d6705e",
      "role": "SOURCE_LEAD_surgical_task_segmentation"
    },
    {
      "id": "2410.18519v2",
      "title": "Reinforcement Learning Controllers for Soft Robots using Learned Environments",
      "ref": "http://arxiv.org/abs/2410.18519v2",
      "sha256": "730e3d7a5c947fbcc603b4dfff987ade789a5c16ad12ea59fa68f14d0e0b8884",
      "role": "SOURCE_LEAD_soft_robot_learning_environments"
    },
    {
      "id": "1908.05250v1",
      "title": "Swimming locomotion of Soft Robotic Snakes",
      "ref": "http://arxiv.org/abs/1908.05250v1",
      "sha256": "d64d08d6436732c73dfc65d1e766cc33e98b1f04e9906cffa5beaed75679049e",
      "role": "SOURCE_LEAD_soft_robot_locomotion"
    }
  ],
  "out_of_scope_examples": [
    {
      "id": "2512.03307v1",
      "title": "Robust Tabular Foundation Models",
      "reason": "Returned by broad foundation-model search but not robotics-specific enough for this packet."
    }
  ],
  "workbench_requirements": [
    "Every sim-to-real claim declares robot morphology, actuator units, sensor units, environment geometry, obstacle state, command log, predicted trace, observed trace, latency, tolerances, and negative controls.",
    "Vision-language-action and foundation-model claims require benchmark cards, task suites, hidden-test hygiene, action budgets, contamination checks, and real-robot safety boundaries.",
    "Surgical, medical, and rehabilitation robotics claims require clinical non-claim language unless primary clinical evidence, approvals, and domain review receipts exist.",
    "Soft-robotics and biology-inspired claims require material model assumptions, actuation physics, environment coupling, and measured deformation traces before promotion.",
    "BuildLang/buildc should carry typed units, kinematics/dynamics kernels, trace schemas, safety envelopes, and verifier receipts after the JavaScript fixture stabilizes."
  ]
}