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Does Evo-1 run on Franka Emika Panda?

partial
T1 · upstream documentedlast verified 2026-10-25 · todayLeRobot 0.6.1

Scope: Simulation only: LIBERO (simulated Franka Panda); no real-robot training or inference documented.

Capabilities

CapabilityStateNotesEvidence
Train / fine-tuneno verified evidence—
Real-robot inferenceno verified evidence—
Simulation inferenceworksLeRobot v0.6.1 reports 96.65% overall on LIBERO (2,000 episodes) for its reproduced Stage-2 checkpoint; the Evo-1 paper reports 94.8%.ev-lerobot-evo1-libero, ev-evo1-paper-libero, ev-libero-panda-src
Teleop + recordno verified evidence—

Evidence 3 sources; the cell shows the highest tier

  1. T1 · upstream documentedofficial-docssource 2026-08-03verified 2026-10-25 (today)lerobot 0.6.1

    LeRobot v0.6.1 Evo-1 guide: two-stage training on lerobot/libero and a LIBERO evaluation of the released Stage-2 checkpoint (zuoxingdong/evo1_libero).

    “| **Overall** | **1,933** | **2,000** | **96.65%** |”
    https://github.com/huggingface/lerobot/blob/v0.6.1/docs/source/evo1.mdx
  2. T1 · upstream documentedpaperverified 2026-10-25 (today)

    Evo-1 paper (v2): LIBERO is one of its three simulation benchmarks (with Meta-World and RoboTwin).

    “and reaches 94.8% on LIBERO, demonstrating its adaptability in both single-arm and dual-arm manipulation tasks.”
    https://arxiv.org/abs/2511.04555v2
  3. T1 · upstream documentedsource-codesource 2025-03-15verified 2026-10-25 (today)

    LIBERO's tabletop tasks instantiate a Mounted<robot> model (libero_tabletop_manipulation.py); its MountedPanda robot class is the Franka Panda.

    “Panda is a sensitive single-arm robot designed by Franka.”
    https://github.com/Lifelong-Robot-Learning/LIBERO/blob/8f1084e3132a39270c3a13ebe37270a43ece2a01/libero/libero/envs/robots/mounted_panda.py

Evo-1 inference speed by hardware robot-independent · inference cost only

ComputeRuntimePrecisionCamsp50 / p95 msInferencePlatformSource
Jetson Orin Nano 8GB (Super)ONNX (split)fp161289.18 / 292.123.45 Hzjetpack 7.2 (L4T R39.2.x, per the repo host-setup doc at this commit) · power_mode MAXN_SUPER, pinned clocks · cuda 13.2 · tensorrt 10.16.2 (system, via ONNX Runtime TensorRT EP)
conditions

board: Jetson Orin Nano Super 8 GB developer kit · os: Ubuntu 24.04 · onnxruntime-gpu: 1.24

Inference cost only; not task success. Deterministic synthetic observations, pinned MAXN_SUPER clocks. Untrained action head: measures inference cost of the EVO1 architecture only; the author marks it as never to control a robot. Author labels the precision "mixed FP16".

T3 · community reported
Jetson Orin Nano 8GB (Super)TensorRTfp162 @640x480332.77 / 333.633 Hzjetpack 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 448x448; 32 denoising steps; latency is runtime-only over a pre-materialized observation ring. Whole-board RAM in use 2.76 GB (unified memory, desktop running). Actions were gated against the stock policy (cosine >= 0.99999) before timing.

T3 · community reported
Jetson Orin Nano 8GB (Super)ONNX (split)fp162414.67 / 424.722.41 Hzjetpack 7.2 (L4T R39.2.x, per the repo host-setup doc at this commit) · power_mode MAXN_SUPER, pinned clocks · cuda 13.2 · tensorrt 10.16.2 (system, via ONNX Runtime TensorRT EP)
conditions

board: Jetson Orin Nano Super 8 GB developer kit · os: Ubuntu 24.04 · onnxruntime-gpu: 1.24

Inference cost only; not task success. Deterministic synthetic observations, pinned MAXN_SUPER clocks. Author labels the precision "mixed FP16". System RAM 6001 MB in use.

T3 · community reported

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History

  1. 2026-10-25 fecccd1 Initial release: robot policy compatibility matrix · Danilo Znamerovszkij