Does GR00T N1.7 run on Franka Emika Panda?

works with caveats

T1 · upstream documentedlast verified 2026-10-24 · 1d agoIsaac GR00T

Capabilities

Capability	State	Notes	Evidence
Train / fine-tune	works		ev-groot-droid-example
Real-robot inference	works with caveats	Deployment path documented; no published real-robot results.	ev-groot-droid-example, ev-groot-droid-server, ev-droid-paper
Simulation inference	no verified evidence		—
Teleop + record	no verified evidence		—

Caveats

NVIDIA documents the DROID deployment path (inference server + DROID robot control script) but publishes no on-robot DROID success rates for N1.7. The README reports only offline MSE on demo data.
Targets the DROID platform (ZED cameras, DROID control package), not an arbitrary Panda setup. The DROID software accepts either a Panda or an FR3.

Evidence 3 sources; the cell shows the highest tier

T1 · upstream documentedofficial-docssource 2026-04-18verified 2026-10-24 (1d ago)
Isaac-GR00T n1.7-release DROID example: zero-shot via the OXE_DROID embodiment tag or the nvidia/GR00T-N1.7-DROID fine-tune, fine-tuning commands, and a robot control script (main_gr00t.py) for the DROID platform.
“The N1.7 base model supports DROID inference out of the box via the `OXE_DROID_RELATIVE_EEF_RELATIVE_JOINT` pretrain tag.”
https://github.com/NVIDIA/Isaac-GR00T/blob/23ace64f17aa5015259b8609d371eb61a357c776/examples/DROID/README.md
T1 · upstream documentedofficial-docssource 2026-04-18verified 2026-10-24 (1d ago)
The same README documents an inference server for real-world deployment and a client that runs on the DROID control laptop.
“Inference Server (for real-world deployment)”
https://github.com/NVIDIA/Isaac-GR00T/blob/23ace64f17aa5015259b8609d371eb61a357c776/examples/DROID/README.md
T1 · upstream documentedpapersource 2025-04-22verified 2026-10-24 (1d ago)
The DROID platform and dataset are built on the Franka Emika Panda.
“We chose the Franka Emika Panda 7 DoF robot arm as the base of our setup since it is widely adopted in the robot research community, reliable, relatively affordable and was available at most participating institutions.”
https://arxiv.org/abs/2403.12945v2

GR00T N1.7 inference speed by hardware robot-independent · inference cost only

Compute	Runtime	Precision	Cams	p50 / p95 ms	Inference	Platform	Source
H100 80GB	TensorRT	bf16	2 @256x256	22.9 / —	43.67 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
RTX PRO 6000 Blackwell Server	TensorRT	bf16	2 @256x256	24.7 / —	40.49 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
L40	TensorRT	bf16	2 @256x256	35 / —	28.57 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson AGX Thor 128GB	TensorRT	mixed	2 @256x256	35.6 / —	28.09 Hz	jetpack 7.2 · power_mode MAXN with jetson_clocks · cuda 13.2 · torch 2.13.0 conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Mixed NVFP4 quantization (enum has no fp4 value).	T3 · community reported
RTX PRO 6000 Blackwell Server	torch.compile	bf16	2 @256x256	52.1 / —	19.19 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
H100 80GB	torch.compile	bf16	2 @256x256	53.4 / —	18.73 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
L40	torch.compile	bf16	2 @256x256	66.8 / —	14.97 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
RTX PRO 6000 Blackwell Server	PyTorch eager	bf16	2 @256x256	75.7 / —	13.21 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson AGX Thor 128GB	TensorRT	bf16	2 @256x256	76.8 / —	13.02 Hz	jetpack 7.2 · power_mode MAXN with jetson_clocks · cuda 13.2 · torch 2.13.0 conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson AGX Thor 128GB	torch.compile	bf16	2 @256x256	100.5 / —	9.95 Hz	jetpack 7.2 · power_mode MAXN with jetson_clocks · cuda 13.2 · torch 2.13.0 conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
H100 80GB	PyTorch eager	bf16	2 @256x256	101.5 / —	9.85 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson AGX Thor 128GB	PyTorch eager	bf16	2 @256x256	106.8 / —	9.36 Hz	jetpack 7.2 · power_mode MAXN with jetson_clocks · cuda 13.2 · torch 2.13.0 conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
L40	PyTorch eager	bf16	2 @256x256	123.7 / —	8.08 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson Orin Nano 8GB (Super)	TensorRT	fp16	3 @640x480	273.7 / 275.4	3.66 Hz	jetpack 7.2.1 · power_mode MAXN_SUPER, clocks pinned with jetson_clocks (GPU 1020 MHz, CPU 1728 MHz) · tensorrt 10.16.2.10 conditions board: Jetson Orin Nano Engineering Reference Developer Kit Super (8 GB) · l4t: R39.2.1 · os: Ubuntu 24.04.4 LTS · python: 3.12.3 · desktop: running Inference cost only; not task success. 300 s run on synthetic 640x480 observations resized by the exported policy to 256x256; 4 denoising steps; latency is runtime-only over a pre-materialized observation ring. Whole-board RAM in use 5.78 GB (unified memory, desktop running). Actions were gated against the stock policy (cosine >= 0.99999) before timing. Three camera views, each with two frames (GR00T N1.7 temporal input).	T3 · community reported

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History

2026-10-25 fecccd1 Initial release: robot policy compatibility matrix · Danilo Znamerovszkij