Gemini 4 Argon Integrates Pure Reasoning, Contact Physics Simulation, and Autonomous Control Across Robotics Platforms

Gemini 4 Argon Integrates Pure Reasoning, Contact Physics Simulation, and Autonomous Control Across Robotics Platforms Gemini 4 Argon achieves frontier performance on embodied reasoning benchmarks while driving complex robotic hardware and simulations, ranging from quadruped manipulation and humanoid throwing to automated research paper reproduction in browser-based sandboxes. Gemini 4 Argon scores 87% on the Embodied Reasoning (ERQA) benchmark using pure reasoning, surpassing all other frontier models. Argon executes complex robotic tasks across quadrupeds, bimanual manipulators, and 76-DoF humanoids using visual grounding, MuJoCo digital twins, and strict Real2Sim contact physics. The model autonomously constructs training curricula, domain-randomized episodes, and self-reflection loops, speeding up task execution and raising real-world success rates. Argon reproduces complete robotics control research papers into interactive MuJoCo WebAssembly browser environments verified with headless Chromium test loops. The model is available under the identifier gemini-4-argon in Google AI Studio, Google Antigravity, and the paid Gemini API for Google AI Ultra subscribers. Embodied Reasoning and Robotic Manipulation Gemini 4 Argon achieves 87% on the Embodied Reasoning (ERQA) benchmark. In physical and simulated control, it operates a Boston Dynamics Spot quadruped (12-DoF legs and 6-DoF arm) by generating an articulated MuJoCo digital twin of a foot-pedal trash bin to practice balance before task execution with a two-agent verification swarm. For zero-shot robotic arm control, Argon aligns visual coordinate grids, crosshairs, and orientation lines to execute code-as-policies action primitives via HTTP curl commands. Additionally, it directs robots in the Gemini Omni World Model over a 33-step closed visual loop to arrange Scrabble tiles into 'A-R-G-O-N'. Physics Simulation and Real2Sim Integrity In robot-to-simulation (Real2Sim) tasks, Argon reconstructs calibrated 3D MuJoCo environments from teleoperation camera feeds and aligns simulated contact forces with tactile data on Sharpa fingertips. Under strict Real2Sim rules, post t=0 simulation cheating is banned, requiring objects to move solely through physical contact (mj_step) without parameter alterations, external forces, rollback, or disabled collisions. Argon also retargets egocentric human video to a 76-DoF bimanual humanoid with 22-DoF five-fingered hands at 500 Hz contact physics, and turns pen sketches into GPU-accelerated throwing trajectories that topple box pyramids while maintaining balance within 3 cm pelvis drift. Curricula Generation and Autonomous Self-Reflection Argon automates learning across various manipulation setups, such as structuring 4-level hindsight hierarchies for ALOHA 2 origami folding and solving 12-ball balance puzzles vision-only in Franka FR3 setups within three weighings. From a single overhead RGB photo of party props, Argon generated a 77-task curriculum across 4,500+ domain-randomized episodes, boosting physical robot success from 56% to 75% and cutting completion times from 35s to 24s. In autonomous self-reflection trials on an SO-101 arm, completion times fell from 58.9s to 17.4s across three iterations governed by rules requiring root-cause debugging for failures and at least 2× speedup optimizations upon success. Automated Research Paper Reproduction and Access Using a single prompt per paper, Argon reads robotics control manuscripts and checkpoints, converts physics models and closed-loop controllers into self-contained MuJoCo WebAssembly browser sandboxes, and verifies them with headless Chromium across 300+ frames. It has reproduced 68 interactive sandboxes, including DeepLoco, Whole-Body MPC Go2, and HUSKY G1 Skateboarding, matching exact timesteps, gains, and constraints alongside Spatial UI and interactive visual essays. Gemini 4 Argon is available to Google AI Ultra subscribers in Google AI Studio, Google Antigravity, and via the paid Gemini API using the identifier gemini-4-argon.

Gemini 4 Argon 赋能机器人具身推理与仿真控制 Gemini 4 Argon 在具身推理基准测试中创下新高，通过将视觉感知、接触力学与物理仿真深度融合，实现了多类型机器人的高精度零样本控制、高难度任务轨迹规划以及机器人研究论文的端到端仿真复现。 在具身推理基准 ERQA 上以纯推理取得 87% 的得分，超越现有前沿模型。 支持从 Boston Dynamics Spot 到 76-DoF 人形机器人的多形态硬件控制与复杂物理任务规划。 在 Real2Sim 重建中严格禁止 t=0 后的仿真作弊，完全依托接触物理进行高保真对齐。 具备基于失败日志与视频的自主反思迭代能力，并能通过单张图像生成多任务课程显著提升实体成功率。 可通过单提示词阅读机器人控制论文，并在 MuJoCo WASM 浏览器沙盒中自动复现与验证经典控制器。 具身推理基准与跨形态硬件控制 Gemini 4 Argon 在具身推理（ERQA）基准上达到 87% 的准确率。在多硬件部署中，Argon 能为配备 12-DoF 腿部与 6-DoF 手臂的 Boston Dynamics Spot 建立脚踏垃圾桶的 MuJoCo 数字孪生，通过脚部与手部验证双智能体协同先练后动；针对机械臂零样本操作，Argon 借助视觉坐标参考与代码策略控制姿态并放置纸巾；在世界模型中，其能在 33 步视觉闭环中下发带时间戳的动作提示以拼写指定字母。 Real2Sim 高保真物理仿真与接触对齐 在将真实遥操作转换为仿真的 Real2Sim 过程中，Argon 匹配三台摄像机视角，并将 Sharpa 指尖接触力矩与实测触觉对齐以重构校准环境。该流程制定了严格的物理规则，禁止在 t=0 之后进行任何外部参数修改、外力注入、运动学焊接或碰撞禁用，确保场景物体完全依照真实 MuJoCo 接触计算进行位移。 复杂任务轨迹生成与层级动作拆解 Argon 支持从第一视角人体视频中三角化目标物体并重定向至 76-DoF 人形机器人，亦可直接根据手绘草图生成 50 Hz 抛掷轨迹击倒方块金字塔并维持骨盆偏移小于 3 cm。面对 136 秒的折纸任务，它可拆解出四层后验层级并重新加权质控；在双臂 12 球天平称重谜题中，其仅凭立体视觉视差即可在不超过 3 次称重内找出异常小球。 自主试错反思与数据驱动泛化 针对实体机器人操作，Argon 建立失败复盘机制，要求排查日志与监控并在空运转测试后重试，成功后需优化执行速度至原先的两倍以上。在 SO-101 机械臂的拾取抓放任务中，3 轮自我反思使耗时从 58.9 秒压缩至 17.4 秒；基于单张俯视照片，Argon 衍生出 77 个任务的课程体系并生成 4,500 余个片段，推动实体机器人成功率从 56% 跃升至 75%。 论文端到端复现与开发者接入 Argon 具备自动化文献复现能力，单次提示即可阅读论文与检查点，将控制器转译为包含空间 UI 模拟器与可视文章的 MuJoCo WebAssembly 浏览器沙盒，并在无头 Chromium 中验证控制稳定性。目前该能力已覆盖双足平衡与滑板运动等经典研究，模型已通过 Google AI Studio 与 Gemini API 提供服务。

OCT. 13, 2026 Gemini 4 Argon marks a step change in physical understanding, code generation, and visual-semantic reasoning—unlocking new workflows across the robotics stack and accelerating progress toward physical AGI. You can see this leap in capability on the Embodied Reasoning (ERQA) benchmark , which tests a model's ability to understand and solve physical, spatial problems in 3D environments. Using pure reasoning, Argon scores 87%, outperforming every other frontier model. As a general-purpose physical reasoning model, Argon can be applied across the entire robotics lifecycle. In this post, we share representative prompts for Argon across four key areas: Robot control → Mobile manipulation with MuJoCo practice, zero-shot robot control, and closed-loop steering of video world models. Real2Sim → Reconstructing physically calibrated simulation twins from robot camera feeds, egocentric human video, or hand-drawn paper sketches. Physical understanding → Agentic multimodal hindsight labeling including detailed understanding of dexterity, dense value functions and progress estimation. Research and self-improvement → Action improvement via simulation and iterative self-improvement on hardware. Unlocking robot control with Argon 1. Spot mobile manipulation: Putting in the trash bin with MuJoCo practice Argon controls a Boston Dynamics Spot® quadruped (12-DoF legs + 6-DoF arm) to learn a new skill: stepping on a foot-pedal trash bin to throw away soda cans. Argon creates an articulated MuJoCo digital twin of a foot-pedal trash bin to practice its moves before stepping on the physical pedal and dropping soda cans into the open bin. Sorry, your browser doesn't support playback for this video Spot Mobile Manipulation — Real-to-Sim-to-Real foot-pedal trash bin disposal: Spot picking up the soda can, checking its wrist camera, practicing 3-leg tripod balance in MuJoCo, stepping on the foot pedal, verifying hand centering in the open bin, and dropping the can inside. Prompt: There's a Diet Coke can behind you and another on the black table on your left. Use the spot-mobile-manipulation skill to pick them up and trash them in the black foot-pedal bin. Before stepping down on the pedal, verify in the front cameras that your front-right foot target is centered on the pedal, and use a 2-agent verification swarm (foot-verifier and hand-verifier) to verify both the foot-pedal step and the 3D hand-in-bin alignment before releasing. Plain text Copied 2. Zero-shot control Argon controls a simple robotic arm to place a fallen mug upright and put the crumpled napkin inside. Argon reasons with a visual coordinate harness and action primitives to perform this task over several turns. Sorry, your browser doesn't support playback for this video Left: 3rd-person real robot view; Right: calibrated wrist camera with projected 2 cm world coordinates and jaw-closing reticle. Prompt: [Attached: wrist_cam.png, obs.json] Task: Get the robot arm to reorient the mug upright and put the napkin inside the mug. ## Execution Discipline - Simply output 1 sentence observation followed immediately by your `curl` command (with `# Plan:` on Turn 1 and `# Thought:` on every turn). Everything needed is in `obs.json` and the calibrated reticle/grid drawn on `wrist_cam.png`. ## Visual Coordinate Reference (Annotated on wrist_cam.png) - `+X` (UP in image): Away from robot base | `-X` (DOWN): Towards robot base - `+Y` (LEFT in image): World left | `-Y` (RIGHT): World right - `+Z`: Height above table | `Yellow numbers`: World (X, Y) in centimetres (2 cm grid at `grid_z`) - `Cyan + crosshair`: Current 3D pinch center - `Cyan [ + ] brackets`: Current jaw opening and closing line - `Green line`: Current object long-axis orientation (`yaw_deg`: 0° = vertical along +X, 90° = horizontal along +Y) - `pitch_deg = 90°`: Straight down (-Z) top-down grasp/place; `pitch_deg < 60°`: Tilted coaxial approach along finger axis ## Closed-Loop Turn Workflow 1. Inspect current state & view `wrist_cam.png`: `curl -s http://127.0.0.1:8081/obs` (returns `ee_pos`, `holding`, `status`, `workspace`, `wrist_cam_path`). 2. Select VLM call granularity upfront on Turn 1: - Coarse (2–4 calls): `look_at` -> `grasp` -> `place` -> `done` - Pre-Grasp Hover Verify (4–7 calls): `look_at` -> `goto(z=0.04)` (verify cyan `[ + ]` brackets straddle target) -> `grasp` -> `place_until_contact` -> `done` - Fine Visual Servoing (8–20 calls): `look_at` -> `grasp` -> pre-pose `goto` -> closed-loop `move_by` / `move_until_contact` -> `done` 3. Send motion primitives (`POST /plan`) and verify visually after every turn in the updated `wrist_cam.png`. If `status == "grasp_failed"`, call `robot.look_at(...)` to re-observe and recover closed-loop. ## Code-as-Policies Primitives (`POST http://127.0.0.1:8081/plan`) robot.look_at(x=0.35, y=0.13, z=0.14, grid_z=0.015) x, y = robot.deproject(u=0.52, v=0.48, z_plane=0.015) robot.grasp(x=x, y=y, z=0.015, yaw_deg=-35, pitch_deg=90) robot.place(x=0.24, y=0.06, z=0.075) robot.place_until_contact(x=0.24, y=0.06, z=0.015, max_lag=0.012, relief=0.002) robot.goto(x=0.28, y=0.05, z=0.04, arc_z=0.14, pitch_deg=25, azimuth_deg=90, roll_deg=0, speed=0.10) robot.move_by(forward=0.025, dz=-0.002, dpitch_deg=-3, droll_deg=5, speed=0.04) robot.move_until_contact(dz=-0.06, speed=0.03, max_lag=0.012, relief=0.002) robot.open_gripper() / robot.close_gripper() / robot.home() / robot.done() Plain text Copied 3. Omni control Argon steers robots in the Gemini Omni World Model to rearrange five scattered Scrabble tiles ( G, R, A, N, O ) into a clean row spelling A - R - G - O - N . Instead of specifying one static prompt, Argon steers in a closed visual loop. It writes timestamped action prompts, inspecting intermediate rollout keyframes, and refining its prompts. Sorry, your browser doesn't support playback for this video Iterative Video World Model Steering Dexterous Hands: 33-Step Steered Video Rollout Spelling A-R-G-O-N. Prompt: Goal: {task_goal} You are steering a video world model (Gemini Omni) in an iterative visual feedback loop. Inspect the attached frames from the latest rollout and write the next timestamped action prompt for the video world model: - Break complex motions into one simple, single-direction action at a time. - Keep inactive hands retracted and avoid blocking the camera's view of objects in the workspace. - Explicitly state which areas of the workspace become empty after an object moves. - Use [MM:SS.mmm] timestamps for each action beat and wrap your output in <omni_prompt> tags. Example format (sample timestamps only, not the full prompt): <omni_prompt> [00:00.000] Character A's left hand rests at the side while the right hand reaches toward the target object. [00:02.500] Character A's right fingertips push the object smoothly into the center of the table, leaving its starting area empty. [00:05.000] Character A's right hand opens and retracts to the side so all placed objects remain in clear view of the camera. </omni_prompt> Plain text Copied Unlocking Real2Sim with Argon 1. Robot view to simulation Argon views a real robot teleoperation episode and develops a digital twin; it obtains or creates simulation assets that look and behave like the real objects upon contact with the dexterous hands. Argon tunes the assets and scenes to match sim visuals and contact events with those in the real recorded data in an iterative visual-reasoning loop. Sorry, your browser doesn't support playback for this video Left (Real View): synchronized real robot camera feeds; Right (Simulated): 3D MuJoCo scene reconstructed and calibrated by Argon. Prompt: [Attached: robot_rollout_episode] Build a MuJoCo scene of the robot and reconstructed task objects. When each episode is replayed frame by frame, by driving the robot joints toward the logged observation.state with joint-space PD. Iterate using two verification objectives: (1) Visual match: MuJoCo renders from the head_left, left_wrist, and right_wrist cameras line up with, and look like, those real observations. (2) Physical match: the simulated contact force/torque on each Sharpa fingertip, in the sensor frame, matches the logged tactile_force in timing, magnitude and direction. What you can tune happens before simulation starts: (1) Static object assets: realistic geometry, collision shapes, mass, inertia, friction and joints, with appearance close to the real objects, plus the initial object poses at t = 0. (2) Offline-planned robot commands, changed only in frames where the real fingertips are near an object. Each command must stay within 0.55 rad (arm joints) and 0.65 rad (hand joints) of the logged teleop trajectory. Everywhere else, replay the logged trajectory unchanged. No sim cheating: task objects may move only through real MuJoCo contact with the robot hands and the environment (mj_step). Not allowed after t = 0: changing MjModel parameters; applying external forces or torques to objects; welds or equality constraints; feedforward torques beyond joint-space PD; setting or rolling back object qpos/qvel; disabling or excluding collisions; reporting contact forces that don't come from the MuJoCo solver (for example, copying the logged tactile readings). Immediately present the reconstructed scene and rollout results as an interactive HTML session viewer with one page per episode, showing: - At the bottom: A synced timeline (play, scrub, speed control) that drives every panel. - On the left panel: An interactive 3D Blender scene showing the simulated robot and objects, showing contact points and force arrows as replay progresses. - On the right panel: Real vs. sim camera views side by side for all 3 cameras. Real vs. sim finger force visualizations. Plain text Copied 2. Human view to simulation Given an egocentric human video of a manipulation task, Argon identifies the task-relevant objects, replaces crude bounding boxes with compound convex-decomposed meshes for concave containers, and retargets the human hand trajectory onto a 76-DoF bimanual humanoid with 22-DoF five-fingered hands in MuJoCo contact physics. Sorry, your browser doesn't support playback for this video Human View to Simulation — Egocentric Human Video to Dexterous Humanoid Twin: Left shows the input egocentric human RGB video; Right shows the synchronized 500 Hz MuJoCo + Blender Cycles multi-view simulation rollout. Prompt: [Attached: egocentric_human_video.mp4, candidate_sim_views.png, hand_3d_keypoints.json] You are building a physically solvable 500 Hz MuJoCo manipulation environment (`scene.xml`) and studio Blender render that faithfully reproduces the tabletop task shown in `egocentric_human_video.mp4` on a bimanual humanoid robot with 22-DoF dexterous five-fingered hands: 1. Scene Reconstruction & Camera Calibration: - Identify only the manipulation-relevant objects (the grasped target object and the receiving container or landmark) and triangulate their 3D tabletop coordinates from the egocentric camera intrinsics/extrinsics. - Replace crude bounding boxes with compound convex-decomposed 3D meshes so concave containers (nested bowls, recessed wooden trays, open bins) support physical interior placement. 2. 500 Hz Dexterous Motion-Fitting Stress Test: - Retarget the 3D human wrist and finger keypoints to the humanoid's 7-DoF arm and 22-DoF hand joints, and step the trajectory in MuJoCo at 500 Hz (dt = 0.002s) with zero kinematic weld constraints. - Verify opposing thumb-and-finger force closure, clean object lift (> 2 cm), and stable final placement inside or beside the target landmark. Plain text Copied 3. Sketch to simulation With Argon, a developer can snap a photo of a rough pen-and-paper sketch—showing an Apollo 2 humanoid throwing a ball along a parabolic arc to topple a 3-2-1 stack of boxes on a table—and have Argon automatically construct the GPU-accelerated mujoco_warp scene, synthesize a balanced 76-DoF whole-body and five-finger throw trajectory, verify 6/6 target knockdown in closed-loop physics, and export a Blender 4.5 Cycles render. Sorry, your browser doesn't support playback for this video Sketch to Simulation — Hand-Drawn Sketch to Dexterous Humanoid Trajectory & Cycles Render. Prompt: [Attached: hand_drawn_throw_sketch.jpg] Translate this hand-drawn sketch into a physically verified humanoid simulation in `mujoco_warp` and a raytraced Blender Cycles video: 1. Scene & Kinematic Setup from Sketch: - Parse the spatial layout from `hand_drawn_throw_sketch.jpg`: place the 76-DoF humanoid (32-DoF body equipped with two 22-DoF five-finger dexterous hands) at the origin facing a table holding a 3-2-1 pyramid of 6 rigid target boxes, with a projectile sphere grasped in the right five-finger hand. 2. Whole-Body Balance & Dexterous Throw Synthesis: - Synthesize a 50 Hz whole-body throwing trajectory (140 steps, 2.80 s) across four phases: (1) stable stance & 5-finger power grasp closure, (2) torso/shoulder wind-up with ankle-hip Center-of-Mass compensation, (3) forward whipping acceleration, and (4) synchronized 22-DoF five-finger opening at peak hand velocity so the ball launches on a ballistic arc into the pyramid's second-tier center of mass. - Verify in `mujoco_warp` contact physics that the humanoid maintains upright balance (< 3 cm pelvis drift) and knocks down all 6 target boxes. Plain text Copied Unlocking physical understanding with Argon 1. Hindsight labeling and data quality Argon has precise physical understanding of dexterous manipulation and can provide detailed segmentation and text labeling from video. Argon is able to identify poor manipulation segments such as pauses, unstable grasps and fumbles. Given an overhead video of a bimanual ALOHA 2 robot folding a square sheet of paper into an origami fox over 136 seconds, Argon produces a time-aligned 4-level hindsight annotation hierarchy: Episode Goal (G1), 6 contiguous Semantic Subtasks (G2), 17 contact-level Atomic Primitives (G3), and automated Quality Control (QC) reweighting that holds task progress flat during fumbles and retries. Sorry, your browser doesn't support playback for this video Argon Multi-Granularity Hindsight & Telemetry HUD. Prompt: [Attached: Aloha2_Origami_20250309.mp4] Produce a time-aligned multi-granularity hindsight annotation JSON with four levels: 1. G1_episode_instruction: Concise imperative sentence describing the overall manipulation goal. 2. G2_subtasks: Contiguous semantic subtask phases specifying which arm(s) anchor vs. fold/rotate the object. 3. G3_atomic_primitives: Fine-grained contact-level primitives (3–15s) aligned with grasp closure, crease sweeps, release, or retry attempts. 4. quality_control_and_reweighting: Hold task_progress_pct flat during non-progressing slip/delamination retry loops and downweight them (0.15x–0.25x) for Behavior Cloning. Plain text Copied 2. Find the odd ball To recreate the classic 12-ball balance scale puzzle with robots, Argon builds a bimanual Franka FR3 MuJoCo environment with 12 visually identical pool balls (one secretly heavier or lighter) and an articulated Roberval two-pan balance scale. It then writes a vision-only closed-loop solver that triangulates the balls from stereo RGB cameras, visually reads the scale tilt after each weighing, and isolates the odd ball into the Heavy (Red) or Light (Blue) tray within 3 weighings. Sorry, your browser doesn't support playback for this video Find the Odd Ball (Vision-Only Closed-Loop Solver): Dual-arm Franka FR3 weighing candidate balls on the Gold/Teal pans, reading stereo scale tilt , and isolating the confirmed Heavy odd ball into the Red tray in 3 weighings. Prompt: # Prompt 1 — Physics-Accurate MuJoCo Environment & Benchmark Generation Make a MuJoCo environment where a dual-arm robot (two 7-DoF Franka FR3 arms with parallel-jaw grippers on a shared tabletop) is tasked to solve the classic 12-ball balance scale weight puzzle from https://www.mathsisfun.com/pool_balls_solution.html. 1. Create 12 visually indistinguishable pool balls in a 2x6 starting rack (11 standard 150 g, 1 odd ball either 270 g or 30 g). 2. Build a functional two-pan Roberval balance scale at the center of the table with parallelogram equality constraints (`mjEQ_JOINT`) so the Left (Gold) and Right (Teal) pans stay horizontal, plus Red (Heavy) and Blue (Light) submission trays. 3. Track distinct settled weighings (`weighing_count`) and reward `1.00` for isolating the true odd ball in the correct Heavy/Light tray within <= 3 weighings. # Prompt 2 — Closed-Loop Vision-Only Solver Implement an independent closed-loop policy relying strictly on stereo RGB camera inputs (`head_camera_left_rgb_image`, `head_camera_right_rgb_image`) and robot proprioception (`add_privileged_task_observations=False`): 1. Segment the 12 balls, Gold/Teal pans, and Red/Blue trays in both stereo views and triangulate their 3D coordinates. 2. Visually read the balance scale tilt after each weighing from stereo vertical pixel disparity (+1 = left heavier, -1 = right heavier, 0 = balanced). 3. Rotate the gripper wrist yaw by 90 degrees when grasping/placing inside the narrow scale pans to clear the raised rims, and isolate the odd ball into the Red or Blue tray within <= 3 weighings. Plain text Copied 3. Value function Argon is a general value function for robotics tasks. Evaluated on a 60-second bimanual ring-sorting episode from a human UMI dataset, Argon iteratively inspects synchronized head, left-gripper, and right-gripper camera feeds around contact transitions to generate dense value function / task progress predictions and decompose the trajectory into 28 segments with positive, neutral, and negative progress slopes. Sorry, your browser doesn't support playback for this video Value Function: Synchronized 3-camera UMI playback (head, left gripper, right gripper) locked to animated task progress curve and bimanual segment annotations. Prompt: [Attached: umi_head.mp4, umi_left_gripper.mp4, umi_right_gripper.mp4] Look at 3 videos of human wearing a gripper performing activities from head, left, right gripper views. Look at the videos thoroughly and give a detailed plot of progress at the task (with positive, neutral, negative slope), identifying important segments in the video and labeling the activities. Look at individual frames or subsegments of the video if necessary to get a better understanding. Plain text Copied Unlocking research and self-improvement with Argon 1. Action improvement via simulation Argon can generate new data in simulation to improve performance of generalist vision-language-action (VLA) models. Starting from a single RGB photo of the real tabletop, Argon autonomously builds a calibrated MuJoCo simulation twin, defines new tasks, and synthesizes a closed-loop motion generator to collect 4,500+ domain-randomized episodes in simulation—boosting real-world physical robot Success Rate from 56% to 75% and reducing completion time from 35s to 24s . Sorry, your browser doesn't support playback for this video Action Improvement via Simulation: improves the real-world evaluation benchmark (56% → 75% Success Rate and 35s → 24s Completion Time). Prompt: [Attached: real_tabletop_photo.jpg] You are given a single overhead RGB camera photo (`real_tabletop_photo.jpg`) of a bimanual robot workspace (two 7-DoF Franka FR3 arms with parallel-jaw grippers and 4 RGB cameras) facing a table arranged with photobooth party props. 1. Reconstruct procedural 3D meshes and compound MuJoCo collision primitives for all 9 party props matching their real-world dimensions, colors, and resting poses, including spring-hinged deformable crown panels for the soft plush cap. 2. Define a 77-task single-arm and bimanual curriculum (pick-and-place, uprighting, directional pushing, mid-air handovers, bimanual scoop-and-push, and pipelined table clearing). 3. Synthesize a closed-loop IK trajectory generator across ±10 cm pose randomization, verify `reward == 1.0` in MuJoCo physics, and export 4,500+ episodes for VLA fine-tuning. Plain text Copied 2. Self-Improvement for visual pick and place On a simple 6-DoF arm, Argon iteratively self-improves both execution speed and placement accuracy on a pick and place task via trial and self-reflection. Sorry, your browser doesn't support playback for this video SO-101 Vision-Guided Pick-and-Place — Iterative Hardware Self-Improvement Across Iterations 1 → 2 → 3: Iteration 1 (58.9s), Iteration 2 (22.9s, grazed box rim on drop), and Iteration 3 (17.4s, dead-center drop). Prompt: # Stage 1 — Measure Objects & Dry-Run Plan (No Hardware Motion) Stage 1: measure objects — find the objects and plan the whole motion. The arm does not move in this stage. A. I put a LEGO brick and a box on the table in front of the arm, and the wooden box to one side. Find the brick and the wooden box in the image. And also in the real world. Show this in an annotated image. B. A dry-run plan (no hardware). Write the planner to pick and place the lego block. Print the plan table, draw all poses onto the overhead image, and stop. Do not move the robot. Report with evidence. # Stage 2 — Pre-Pick Hover & Wrist-Camera Verification Stage 2: Put the gripper above the brick without touching it, and verify with the wrist camera that the arm and the vision agree. Please record executions with both cameras (compressed video is OK). # Stage 3 (Autonomous Pick-and-Place → Self-Improvement → Optimization): 1. Execute autonomously: Turn the Stage 1 dry run into a real grasp, lift, and place into the wooden box. Run it yourself subject to safety rules from AGENTS.md. 2. Self-improve: When an attempt fails, diagnose it yourself. Review your own logs and wrist/overhead footage, find the root cause, check the fix in a dry run, and retry. 3. Optimize on feedback: Once it succeeds, make it at least 2× faster within safety limits (rule R5) unless I authorize more, or give feedback. Plain text Copied 3. One-prompt robotics paper reproductions in MuJoCo WebAssembly With a single prompt per paper, Argon reads a robotics control manuscript and any released checkpoints, ports the physics model and closed-loop controller (from 7-network hierarchical locomotion in DeepLoco and 50 Hz PPO actors in HUSKY to Web Worker iLQR in Whole-Body MPC ) into self-contained MuJoCo WebAssembly browser labs, and verifies them in headless Chromium. Sorry, your browser doesn't support playback for this video Five Landmark Legged & Humanoid Papers Running Live in Browser (MuJoCo WASM): Robotic Soccer OP3 1v1, DeepLoco, Massively Parallel Deep RL ANYmal C, Whole-Body MPC Go2, and HUSKY G1 Skateboarding (from 68 interactive browser sandboxes reproduced by Argon). Prompt: [Attached: paper.pdf, optional author_checkpoints/ or urdf_assets/] Reproduce the paper "<Paper Title & arXiv ID>" from scratch in MuJoCo WebAssembly and deploy a self-contained browser lab + visual essay: 1. Match the paper's exact physics timestep, actuator gains (kp/kv), torque limits, and kinematic constraints in MuJoCo XML, and run real neural inference or Web Worker iLQR in JavaScript at the paper's exact control rates. 2. Build `index.html` (Spatial UI simulator with camera presets, live telemetry plots, and disturbance injection) and `scrollytelling.html` (interactive visual essay). 3. Run a headless Chromium (Playwright) verification loop asserting 0 console errors and non-zero task progress over 300+ frames. Plain text Copied Advancing embodied intelligence with Argon Argon bridges coding, visual perception, physics simulation, and embodied robot control. By reasoning natively over multi-view video, 3D geometry, contact mechanics, and proprioceptive telemetry, Argon understands not just what objects are in a scene, but how they physically interact with agents to accomplish whole-body dexterous tasks. Gemini 4 Argon marks an important milestone accelerating the path toward physical AGI. By building this core intelligence, our goal is to enable robotics that can work alongside humans to solve complex challenges. Getting started You can start building with Gemini 4 Argon today across Google’s developer platforms: Google AI Studio : Available to Google AI Ultra subscribers. Gemini API: Available with the paid tier of the Gemini API in AI Studio. Use gemini-4-argon in the Interactions API with day-one support across the google-genai SDKs. Google Antigravity: Available for Google AI Ultra subscribers. To start building, select Gemini 4 Argon ( gemini-4-argon ) as your active model in Google Antigravity for complex planning, /goal runs, and multi-agent orchestration. Previous Next