RAMBO Go2 policies
Final policy checkpoints produced by reproducing RAMBO: RL-augmented Model-based Optimal Control for Whole-body Loco-manipulation.
Included checkpoints
| Task | Checkpoint | Training configuration |
|---|---|---|
Isaac-RAMBO-Quadruped-Go2-v0 | quadruped/model_2000.pt | 4096 environments, 2000 PPO iterations, seed 42 |
Isaac-RAMBO-Biped-Go2-v0 | biped/model_4000.pt | 4096 environments, 4000 PPO iterations, seed 42 |
Each model_*.pt is a CRL2 PyTorch training checkpoint. It contains the
policy, value network, optimizer state, observation normalizer, and completed
iteration count. It is not a Transformers model or an inference-only
safetensors file.
The accompanying agent.yaml and env.yaml files record the training
configuration. The checkpoint paths intentionally exclude training videos,
TensorBoard events, W&B files, and Python pickle configuration files.
SHA-256 checksums:
quadruped/model_2000.pt 1cc5f68fe15e37ccabae26060d79a26a8c078ed465a81f6f729c2009b67ca706
biped/model_4000.pt c16e64bf1ca2dc16878c386b742cd303e65040f52e8744cd0c96c540c595b2a6
Use with RAMBO
Use the matching RAMBO/Isaac Lab environment and place a downloaded checkpoint
under the corresponding CRL2 log root before launching play.py. For example,
for the Quadruped policy:
hf download dontKnow23456/rambo-go2-policies quadruped/model_2000.pt \
--local-dir /tmp/rambo-go2-policies
mkdir -p logs/crl2/rambo_quadruped/hf_quadruped/params
cp /tmp/rambo-go2-policies/quadruped/model_2000.pt \
logs/crl2/rambo_quadruped/hf_quadruped/
python scripts/reinforcement_learning/crl2/play.py \
--task Isaac-RAMBO-Quadruped-Go2-v0 \
--num_envs 1 \
--load_run hf_quadruped \
--load_checkpoint 2000
For Biped, substitute task Isaac-RAMBO-Biped-Go2-v0, log root
rambo_biped, run name hf_biped, and checkpoint 4000.
These checkpoints were generated from source commit
6f6c87c614255a5ada063da40206a2fa67054cb6 of
catachiii/rambo, using Isaac Sim 4.5.0,
Isaac Lab, CRL2, and PyTorch. The local reproduction includes checkpoint
resume/normalizer-state fixes; use the accompanying workspace source for the
most reliable loading behavior.
env.yaml is an Isaac Lab configuration dump that contains NumPy-specific
YAML tags. Treat it as trusted project configuration and load it only within a
trusted RAMBO/Isaac Lab environment.
License and citation
The source RAMBO repository is licensed under
CC BY-NC 4.0. These
artifacts are shared under the same non-commercial terms; see
LICENSE-CC-BY-NC-4.0.md.
If you use RAMBO, please cite:
@article{cheng2025rambo,
title={RAMBO: RL-augmented Model-based Optimal Control for Whole-body Loco-manipulation},
author={Cheng, Jin and Kang, Dongho and Fadini, Gabriele and Shi, Guanya and Coros, Stelian},
journal={arXiv preprint arXiv:2504.06662},
year={2025}
}
Safety
PyTorch .pt files are pickle-based checkpoints. Only load files obtained
from trusted sources, and use them in simulation before considering any
hardware deployment.