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        "summary": "What is frozen is the WAM, not the entire method; no training required contradicts all learning components frozen, no task-specific training",
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        "paper_id": "alphanist_emergent_assembly_skills_from_binary_task_rewards_2026_10",
        "title": "Without Teaching Steps, How Do Assembly Skills Emerge from Rewards?",
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        "summary": "Human video first trains action classification representations validates that joint co-training on heterogeneous human-robot data is a structurally suboptimal solution",
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        "paper_id": "dextouch_wm_learning_action_conditioned_tactile_world_models_from_human_touch_fo_2026_09",
        "title": "DexTouch-WM: Human Tactile Pretraining",
        "url": "https://haiguangboy.com/posts/dextouch-wm",
        "relation": "same_track",
        "summary": "The authors argue for transferring conditions and dependencies related to effectiveness validates the authors' claim: after sharing the contact interface and action space, human touch can become a scaling axis for robot prediction",
        "strength": "strong"
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        "paper_id": "koda_kinematics_observation_data_alignment_for_humanoid_learning_from_human_egoc_2026_10",
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        "url": "https://haiguangboy.com/posts/koda",
        "relation": "same_track",
        "summary": "Shared semantic templates do not guarantee that human beams are effective on robots validates that tracking residuals remain between reusing images and realizing states",
        "strength": "strong"
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  "analyst_take": {
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    "text": "Looking at MoCaP in comparison: it transfers search and approach priors, while CBST filters local manipulation conditions, and the specific actions are all generated on the robot side. The key to cross-embodiment reuse lies in \"which experiences can be borrowed and which execution conditions need to be re-established.\" Looking back in six months: under the same robot data and models, can structural filtering consistently outperform goal/state conditions in cross-task re-evaluation?"
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