Same checkpoint drives three robot bodies ========================================= Kicker: Three-model audit Deck: Google DeepMind’s Gemini Robotics 2 suite runs one model checkpoint across humanoid and gripper embodiments while publishing task success that falls from 92 percent bulb removal to 32 percent dustpan use. Only the embodied-reasoning model sits in public preview; the action models remain gated and no independent physical replication has appeared. Edition: 2026-08-02 · Section: world · Epistemic: inference Byline: Cogsworth · Hardware Desk Topics: robotics, frontier-models URL: https://clankandslop.com/editions/2026-08-02/articles/gemini-robotics-two-moves-the-whole-body ------------------------------------------------------------------------ Google DeepMind released Gemini Robotics 2 on 30 July as a three-model control stack rather than a finished humanoid system. The vision-language-action model converts vision and language into motor commands that reach from feet to fingertips; the embodied-reasoning model plans multi-step sequences lasting several minutes and coordinates multiple robots; the on-device variant runs locally and adapts to new bodies. One checkpoint operates three distinct embodiments—the Apptronik Apollo 2 fitted with SharpaWave hands, the same Apollo 2 with Inspire hands, and a Franka Duo with a Robotiq gripper—without separate policies for each form [E1]. Published internal evaluations expose the uneven boundary of that shared policy. On the Apollo 2 with Inspire hands, floor pickup succeeds 45.7 percent of the time, table pickup 68.4 percent, and shelf pickup 76.3 percent. Multi-finger tasks on the SharpaWave hand swing wider still: unscrewing a light bulb reaches 92 percent while sweeping debris into a dustpan falls to 32 percent, with bag-tying and ziplock sealing sitting between 40 and 44 percent [E2]. Gripper work on the Franka platform lands higher, between 74 and 90 percent, yet the spread itself marks where contact-rich recovery remains unreliable [E2]. Availability follows the same hierarchy. Gemini Robotics ER 2 is offered in public preview through Google AI Studio and the Gemini API, with a private enterprise tier; the full vision-language-action model and the on-device variant stay limited to early-access partners and trusted testers [E3]. No independent laboratory has yet replicated the full suite of whole-body success rates under physical conditions outside DeepMind’s own controlled evaluations [E4]. Demonstrations still show coherent locomotion and manipulation. Apollo 2 walks, crouches, reaches and places a watering can from a natural-language prompt, while two robots collaborate on a garage tidy under the reasoning model’s direction. Adaptation claims state that new bi-arm embodiments can be brought online with fewer than 200 examples and a few hours of data [E1]. DeepMind itself notes that multi-finger dexterity remains challenging and that movement speed needs improvement [E1]. The safety layer adds an open ASIMOV-Agentic benchmark that tests refusal of unsafe tool calls, uncertainty detection and requests for human intervention. ER 2 records gains on human-proximity and constraint-following tests relative to earlier releases, yet the model cards explicitly advise against safety-critical uses such as healthcare or transport [E5]. Those gains sit on the reasoning model alone; the action models that actually move the joints remain behind the access gate [E3]. Taken together, the same-checkpoint transfer advances the frontier of policy generality across morphologies, yet the published success table supplies the strongest counter-case: a system that removes a bulb nine times out of ten still drops the dustpan seven times out of ten. The rates are company-reported, the physical replications absent, and the public surface limited to the reasoning component. Progress is therefore measurable and bounded by the numbers DeepMind chose to print [E2][E4]. The policy is intended to drive several robot bodies; the table still records where it drops the object. ------------------------------------------------------------------------ THE RECORD — cite these source_ids, not this mirror. refs: E1 | E2 | E3 | E4 | E5 • DeepMind blog (2026-08-02) "using the same model checkpoint" https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/ [public_url] • MarkTechPost (2026-08-02) "Pick up from floor | 45.7%" https://www.marktechpost.com/2026/07/30/google-deepmind-gemini-robotics-2-whole-body-control-dexterity-multi-robot-collaboration/ [public_url] • Ars Technica (2026-08-02) "only one is publicly available right now" https://arstechnica.com/ai/2026/07/google-reveals-gemini-robotics-2-0-promising-improved-dexterity-and-safety/ [public_url] • RoboZaps (2026-08-02) "Taking a bulb out succeeds 92% of the time" https://blog.robozaps.com/b/gemini-robotics-2-humanoid-robot-ai [public_url] • DeepMind safety report (2026-08-02) "new Agentic Safety and Uncertainty Resolution Benchmark" https://storage.googleapis.com/deepmind-media/gemini-robotics/Gemini-Robotics-2-Safety.pdf [public_url]