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beshy3752/
eurekaverse-go2-parkour-seed2

Name: eurekaverse-go2-parkour-seed2
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reinforcement-learning

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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.pt is 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.

beshy3752/eurekaverse-go2-parkour-seed2