NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1
NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 — the rear-mounted compute upgrade: Jetson AGX Orin module, up to 275 TOPS, 64GB LPDDR5, backplane dock and cables included. Fits G1 and G1-D.
- NVIDIA Jetson AGX Orin module included — up to 275 TOPS
- 12-core Arm Cortex-A78AE CPU, 2048 CUDA + 64 Tensor cores
- 64 GB 256-bit LPDDR5 (204.8 GB/s) + 64 GB eMMC
- Rear backplane dock purpose-built for the G1's back expansion bay
- Fits Unitree G1 and G1-D
About the NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1
The NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 is the rear-mounted compute upgrade that puts serious edge AI inside the G1 humanoid instead of on a cart beside it: a full NVIDIA Jetson AGX Orin module seated on a purpose-built backplane that bolts into the G1's back expansion bay, delivering up to 275 TOPS of on-robot computing for perception, navigation and machine-learning research. Sold under Unitree part number G1-AGX-DOCK, the nvidia agx orin backplane expansion dock for unitree g1 arrives as a complete kit — the AGX Orin module itself, the dedicated backplane dock and the cable set.
Out of the box, a stock G1 relies on an external workstation or laptop for the heavy compute: policy inference, camera processing and SLAM all run over a tether. The NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 removes that tether. The Jetson AGX Orin module pairs a 12-core Arm Cortex-A78AE CPU with an Ampere GPU of 2048 CUDA cores and 64 Tensor cores, backed by 64 GB of 256-bit LPDDR5 memory at 204.8 GB/s and 64 GB of eMMC storage. At up to 275 TOPS, the module runs multi-camera perception pipelines, vision-language-model inference and reinforcement-learning policies on the robot itself.
The NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 fits the standard G1 and the G1-D. Installation is a rear-panel job: the backplane dock seats in the G1's back expansion bay and the module connects to the robot over the dock's expansion interfaces. If you run a different G1 configuration or an older revision, ask us to confirm fit for your robot before ordering — and if 275 TOPS is not enough headroom, the same dock family is available with NVIDIA AGX Thor for 2,070 TOPS.
What the NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 Adds
A G1 straight from the box is a body waiting for a brain: the robot's own controllers handle motion, but perception, policy inference and mapping are expected to run on an external workstation, tethered over Ethernet. That works on a lab bench and fails in the field — the cable is the first thing that breaks, and the PC is the first thing that does not fit through the door. The NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 closes that gap by moving the brain onto the robot.
The Jetson AGX Orin module inside delivers up to 275 TOPS from a 12-core Arm Cortex-A78AE CPU and an Ampere GPU with 2048 CUDA cores and 64 Tensor cores. In practice that means the G1 can ingest from its camera stack, run detection and depth estimation, and execute a learned locomotion or manipulation policy in real time, on board. Memory is 64 GB of 256-bit LPDDR5 — enough headroom to keep a perception stack and a language model resident at once — with 64 GB of eMMC storage for the software image.
Because the dock lives in the G1's back expansion bay rather than on an external mount, the robot keeps its full mobility: no tether, no trailing cable set, nothing to snag on furniture, door frames or lab equipment. The 15–60 W module power envelope is supplied through the dock, so there is no separate supply to mount or waterproof.
AGX Orin or AGX Thor — Which Dock for Your G1?
Unitree's backplane expansion dock family for the G1 takes two NVIDIA modules. The NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 delivers up to 275 TOPS — the right class for multi-camera perception, vision-language-model inference and reinforcement-learning policies on a mobile humanoid. The AGX Thor variant steps up to 2,070 TOPS for training-grade and large-multimodal workloads. The mounting is the same; only the module changes. Tell us what you plan to run and we will confirm the right dock for your workload.
Which Robots Does the NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 Fit?
The NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 fits the standard G1 and the G1-D, seated in the robot's back expansion bay — the dock is purpose-built for that bay's connector and mounting pattern, and the cable set in the box links the dock to the robot's internal interfaces.
If you run a different G1 revision or a customised configuration, ask us to confirm fit for your robot before ordering — a photo of your robot's back panel is enough for us to check.
- Unitree G1 — supported, back expansion bay
- Unitree G1-D — supported, back expansion bay
- Other G1 revisions or custom configurations — ask us to confirm fit before ordering
Specifications
| Compute module | NVIDIA Jetson AGX Orin ×1 (sold under Unitree part no. G1-AGX-DOCK) |
| AI performance | Up to 275 TOPS |
| CPU | 12-core Arm Cortex-A78AE v8.2 64-bit |
| GPU | Ampere architecture — 2048 CUDA cores + 64 Tensor cores |
| Memory | 64 GB 256-bit LPDDR5, 204.8 GB/s |
| Storage | 64 GB eMMC 5.1 |
| Module power | 15–60 W configurable |
| Mounting | Rear backplane expansion dock, purpose-built for the G1 back expansion bay |
| Compatibility | Unitree G1, Unitree G1-D |
| In the box | 1 × NVIDIA Jetson AGX Orin module · 1 × G1/G1-D dedicated backplane expansion dock · 1 × Cable set |
Shipping & Handling
Every NVIDIA AGX Orin Backplane Expansion Dock for Unitree G1 is 100% quality-inspected before it ships, so it arrives in perfect working order. The kit ships in rigid, shock-absorbing packaging with the AGX Orin module seated and protected in the dock.
The module and dock contain electrostatic-sensitive components — handle the backplane by its edges after unpacking, and ground yourself before seating or reseating the module. A spare G1 battery or charger can travel in the same parcel if you are stocking up for a deployment.
Last updated: September 2026.
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