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eurekaverse-go2-parkour-seed2
beshy3752/eurekaverse-go2-parkour-seed2
eurekaverse-go2-parkour-seed2 is a reinforcement learning model from beshy3752. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
A Unitree Go2 quadruped parkour locomotion policy trained with an LLM-generated environment curriculum (Eurekaverse-style), reproduced on Isaac Lab 3.0.
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Updated Aug 23, 2026
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At a glance
- Task
- Reinforcement Learning
- License
- mit
- Access
- Public
- Created
- Aug 23, 2026
- Updated
- Aug 23, 2026
- SHA
- ea13efe0
Model card
From the Hugging Face model README
Eurekaverse Go2 Parkour Policy — Seed 2
A Unitree Go2 quadruped parkour locomotion policy trained with an LLM-generated environment curriculum (Eurekaverse-style), reproduced on Isaac Lab 3.0.
This is the final policy of seed 2 of a 3-seed reproduction run (independent RL seed + independent LLM-generated curriculum from seed 1).
⚠️ This is the privileged teacher policy (it consumes terrain scandots — a height scan around the robot). It is NOT deployable standalone: without scandots it just collapses. To run on a real robot / another sim, distill a depth-based student first (
train.py --use_camera ...). See the code repo for setup.
What this is
- Task: parkour locomotion — traverse obstacle courses toward 8 sequential goals (ramps, boxes, stepping stones, stairs, gaps, beams, …).
- Training: 5 curriculum iterations × 8 parallel policy runs × 2000 PPO steps/env, resumed from a 1000-step flat-ground walk pretrain. Terrains generated each iteration by
gpt-4o-2024-08-06. This checkpoint is the iteration-4 winner (parallel run 2, lineage [2,4,4,6]). - Checkpoint:
model_11000.pt(final).model_0.ptis the iteration-4 starting point.
Benchmark performance
Held-out benchmark of 20 parkour tasks × 10 difficulty levels, metric = number of goals reached (out of 8):
- This policy (seed 2): 4.52 / 8
- For reference, seed 1's final policy scored 4.36 / 8 — the two independent seeds land within ~0.2, i.e. the reproduction is seed-stable.
Per-task numbers are in benchmark_results.txt.
Files
| file | description |
|---|---|
model_11000.pt | final policy checkpoint |
model_0.pt | iteration-4 starting checkpoint |
legged_robot_config.pkl | pickled (env_cfg, train_cfg) — required to load the policy |
final_iteration_terrain.py | the LLM-generated terrain this policy trained on (iter 4) |
benchmark_results.txt | per-task benchmark evaluation |
How to load / use
Not a standalone / transformers model. Requires Isaac Lab 3.0 + the extreme-parkour / legged_gym env used for training. Place the checkpoint + legged_robot_config.pkl under logs/<proj>/<exptid>/ and load via the repo's task_registry (see scripts/evaluate.py). This is a reproduction/fork ported to Isaac Lab 3.0, so absolute numbers may differ from the original Eurekaverse paper.
Attribution
Built on Eurekaverse (Liang et al., CoRL 2024) and extreme-parkour. Preserve upstream licenses when redistributing.
- Task
- Reinforcement Learning
- License
- mit
- Created
- Aug 23, 2026
- Updated
- Aug 23, 2026