According to @StockMKTNewz, Google (GOOGL) released Gemini Robotics 1.5 today, stating that earlier robots handled single tasks like picking up fruit or zipping a bag, while the new model can reason, plan, and generalize, marking a step beyond single-task execution that traders can timestamp for today. Source: @StockMKTNewz on X (Sep 26, 2025).

According to @drfeifei, the post identifies four open priorities for embodied AI: solving long-horizon, human-centric tasks; efficiently combining low-level control with high-level planning; understanding the generalization limits of current models; and investigating scaling laws for embodied AI, source: @drfeifei. The post presents research questions and does not announce new models, benchmarks, timelines, funding, or partnerships, so it introduces no new quantifiable trading catalyst by itself, source: @drfeifei. Traders should treat this as an agenda-setting signal and monitor future technical disclosures on long-horizon planning metrics, control–planning integration methods, generalization test protocols, and scaling study results before adjusting positions, source: @drfeifei.

According to Fei-Fei Li (@drfeifei), Feature #4 in her thread outlines key manipulation state transitions for embodied AI and robotics, including spatial (next_to, inside, on_top, under, touching), particle coverage (covered, uncovered), thermal (hot, cooked, on_fire, frozen), and control/object states (open, closed, on, off, attached, sliced, diced) (source: Fei-Fei Li, X, Sep 2, 2025). The post provides a concrete taxonomy of task-relevant states for manipulation but includes no datasets, benchmarks, release timelines, companies, pricing, or any references to cryptocurrencies or blockchain integrations (source: Fei-Fei Li, X, Sep 2, 2025).

According to @drfeifei, the demonstration showcases long-horizon mobile manipulation in household-scale scenes with task durations ranging from 1 to 25 minutes (average 6.6 minutes), requiring memory, planning, and long-term reasoning; source: Fei-Fei Li via X, Sep 2, 2025. For traders, the source underscores sustained, sequential decision-making workloads in embodied AI, while providing no commercialization timeline or market metrics; source: Fei-Fei Li via X, Sep 2, 2025. Crypto-focused traders should note the emphasis on long-duration on-device reasoning and memory as a research vector within embodied AI highlighted by the source; source: Fei-Fei Li via X, Sep 2, 2025.