🤖 SRU Navigation Training with Isaac Sim
Reproducible training pipeline for SRU (Spatially-Enhanced Recurrent Units) navigation policy, ported from B2W (wheeled-legged + ZedX depth) to Unitree Go2 (quadruped + LiDAR-derived depth).
Built on NVIDIA Isaac Sim 4.5.0 + IsaacLab v2.1.1, fully containerized with Docker.
✨ Highlights
- Full B2W → Go2 port — assets, cameras, terrain, rewards, termination conditions
- 4-stage training pipeline — Smoke → Cold-start (1024 envs, 30k iter) → Curriculum continuation → PureMaze refinement
- Evaluation toolchain — termination rate stats, baseline comparison, collision analysis, depth ablation
- Env-var driven config — difficulty, terrain, GPU selection, seed — no code edits needed
- Multi-GPU parallel training — independent seeds across GPUs
🏗️ Architecture
┌─────────────────────────────────────────────────────────────┐
│ Docker Container (~30GB) │
│ │
│ ┌───────────────┐ ┌──────────────────────────────────┐ │
│ │ Isaac Sim │ │ IsaacLab v2.1.1 │ │
│ │ 4.5.0 │ │ ┌────────────┐ ┌─────────────┐ │ │
│ └───────┬───────┘ │ │ Nav Task │ │ rsl_rl │ │ │
│ │ │ │ (Go2 Port) │ │ (SRU Fork) │ │ │
│ ▼ │ └─────┬──────┘ └──────┬──────┘ │ │
│ ┌───────────────┐ │ │ │ │ │
│ │ PhysX / GPU │ │ ▼ ▼ │ │
│ │ Simulation │◄──┤ ┌──────────────────────────┐ │ │
│ └───────────────┘ │ │ PPO + ActorCriticSRU │ │ │
│ │ └──────────────────────────┘ │ │
│ └──────────────────────────────────┘ │
│ │
│ Depth → VAE Encoder (64×5×8) → SRU Policy → SE2 cmd │
│ ↓ │
│ policy_go2_jit.pt → Joint Actions │
└─────────────────────────────────────────────────────────────┘
📋 Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| NVIDIA GPU | 12 GB VRAM | 24 GB+ |
| NVIDIA Driver | ≥ 570 | ≥ 570 (⚠️ avoid 595.x) |
| Docker | 24.0+ | Latest |
| NVIDIA Container Toolkit | Installed | Latest |
| Disk Space | 80 GB | 120 GB+ |
| NGC Access | Required | For nvcr.io/nvidia/isaac-sim:4.5.0 |
VRAM Reference
| GPU | NUM_ENVS | GO2_CELL_SIZE |
|---|---|---|
| RTX 5070 12 GB | 768–1024 | 1.0 |
| RTX 4090 24 GB | 2048–2820 | 1.0 / 2.0 |
| A100 80 GB | 8000+ | 1.0 / 2.0 |
🚀 Quick Start
1. Clone & Configure
git clone https://github.com/<your-org>/sru-nav-docker.git
cd sru-nav-docker
cp .env.example .env
# Edit .env — set NGC credentials, GPU index, paths
2. Build the Image
docker compose build # ~30 GB, takes 20-40 min on first build
3. Launch Container
docker compose up -d
docker compose exec sru-nav bash
4. Smoke Test (~30 min)
TASK=Isaac-Nav-PPO-Go2-Dev-v0 NUM_ENVS=24 MAX_ITER=300 \
GO2_DIFFICULTY="0.0,0.4" GO2_INIT_LEVEL=0 \
./scripts/train_go2_scratch.sh
If training starts and loss decreases — you're good to go.
🏋️ Training Pipeline
Stage 1 — Cold Start (36–48h)
TASK=Isaac-Nav-PPO-Go2-v0 NUM_ENVS=1024 MAX_ITER=30000 \
GO2_DIFFICULTY="0.0,0.4" GO2_INIT_LEVEL=0 GO2_CELL_SIZE=1.0 \
SEED=42 RUN_NAME=mixed_scratch_v1 \
./scripts/train_go2_scratch.sh
Stage 2 — Curriculum Continuation
./scripts/continue_go2.sh --iters 10000 --from-iter 7400
Stage 3 — PureMaze Refinement
TASK=Isaac-Nav-PPO-Go2-PureMaze-v0 NUM_ENVS=1024 MAX_ITER=10000 \
./scripts/train_go2_scratch.sh
Evaluation
# Termination rate analysis
PLAY_DIFFICULTY="0.3,0.8" ./scripts/eval_terminations.sh --run-dir <run> --from-iter <iter>
# Baseline comparison
./scripts/compare_baselines.sh
# Collision analysis
./scripts/analyze_collisions.sh
Monitoring
# TensorBoard
docker compose exec sru-nav ./isaaclab.sh -p -m tensorboard.main --logdir logs --bind_all
# Or use Weights & Biases (configure WANDB_API_KEY in .env)
⚙️ Configuration
All training behavior is controlled via environment variables — no code changes needed.
| Variable | Type | Default | Description |
|---|---|---|---|
TASK |
str | Isaac-Nav-PPO-Go2-Dev-v0 |
Gym task ID |
NUM_ENVS |
int | 24 |
Parallel environment count |
MAX_ITER |
int | 1000 |
Max PPO iterations |
SEED |
int | 42 |
Random seed |
GPU |
str | — | GPU device index |
RUN_NAME |
str | auto | Run directory name |
GO2_DIFFICULTY |
"lo,hi" |
cfg default | Difficulty range |
GO2_INIT_LEVEL |
int | 5 |
Initial max terrain row |
GO2_CELL_SIZE |
float | 1.0 |
Terrain tile cell size (m) |
PLAY_DIFFICULTY |
"lo,hi" |
— | Eval-time difficulty override |
Gym Tasks
| Task ID | Purpose | Typical envs |
|---|---|---|
Isaac-Nav-PPO-Go2-v0 |
Cold-start training (mixed terrain) | 1024–2048 |
Isaac-Nav-PPO-Go2-Dev-v0 |
Smoke test / debugging | 24–32 |
Isaac-Nav-PPO-Go2-PureMaze-v0 |
Stage-2 maze refinement | 1024 |
Isaac-Nav-PPO-Go2-Play-v0 |
Playback & evaluation | 20 |
📁 Project Structure
sru-nav-docker/
├── Dockerfile # Image build recipe
├── docker-compose.yml # Container runtime config
├── .env.example # Environment variable template
├── scripts/ # Train / continue / play / eval / diagnostics
│ ├── train_go2_scratch.sh
│ ├── continue_go2.sh
│ ├── eval_terminations.sh
│ ├── compare_baselines.sh
│ └── ...
├── mount/
│ ├── sru-navigation-sim/ # Task definitions + Go2 port
│ │ ├── isaaclab_nav_task/ # Env, rewards, terrain, obs
│ │ └── scripts/ # Entry points
│ └── rsl_rl/ # rsl_rl SRU fork (ActorCriticSRU, PPO)
├── IsaacLab/ # IsaacLab v2.1.1 source
├── assets/baselines/ # Pre-trained baseline checkpoints
├── outputs/logs/ # Training artifacts (TB + ckpt)
└── docs/handover/ # Handover documentation
🔑 Key Dependencies
| Dependency | Source |
|---|---|
| Isaac Sim 4.5.0 | nvcr.io/nvidia/isaac-sim:4.5.0 (NGC login required) |
| IsaacLab v2.1.1 | isaac-sim/IsaacLab |
| rsl_rl (SRU fork) | leggedrobotics/sru-navigation-learning |
| Nav Task | leggedrobotics/sru-navigation-sim |
📖 Citation
If you use this work, please cite the original SRU navigation paper:
@article{yang2025sru,
author = {Yang, Fan and Frivik, Per and Hoeller, David and Wang, Chen and Cadena, Cesar and Hutter, Marco},
title = {Spatially-enhanced recurrent memory for long-range mapless navigation via end-to-end reinforcement learning},
journal = {The International Journal of Robotics Research},
year = {2025},
doi = {10.1177/02783649251401926},
url = {https://doi.org/10.1177/02783649251401926}
}
🙏 Acknowledgments
- ETH Legged Robotics — SRU navigation (original paper & codebase)
- NVIDIA Isaac Sim / IsaacLab — Simulation platform
- Unitree Robotics — Go2 quadruped platform
📄 License
See LICENSE in the repository root.
Sub-components (IsaacLab, rsl_rl) retain their respective licenses.