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Legged Incline Fall-recovery Technique for Enhanced Recovery

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# LIFTER: **L**egged **I**ncline **F**all-recovery **T**echnique for **E**nhanced **R**ecovery

This repository contains the code from my Master's degree research on deep reinforcement learning for quadruped robot fall-recovery control.

📄 **Thesis**: [Deep Reinforcement Learning for Fall-Recovery Control on Non-Flat Terrain of Quadruped Robots](https://www.researchgate.net/publication/378936853_Deep_Reinforcement_Learning_for_Fall-Recovery_Control_on_Non-Flat_Terrain_of_Quadruped_Robots)

📊 **Presentation**: [Canva Slides](https://www.canva.com/design/DAF1_05Jbmw/HriAfzV_RHy3zHZWFr6Aag/view)

![](https://raw.githubusercontent.com/curieuxjy/LIFTER/main/assets/method.png)

> This environment builds on the [legged gym environment](https://leggedrobotics.github.io/legged_gym/) by Nikita Rudin, Robotic Systems Lab, ETH Zurich (Paper: https://arxiv.org/abs/2109.11978) and the Isaac Gym simulator from NVIDIA (Paper: https://arxiv.org/abs/2108.10470).
Training code builds on the [rsl_rl](https://github.com/leggedrobotics/rsl_rl) repository, also by Nikita Rudin, Robotic Systems Lab, ETH Zurich. All redistributed code retains its original [license](LICENSES/legged_gym/LICENSE).

## Robot Platforms

This project uses the **AiDIN** series quadruped walking robots developed for multi-legged locomotion and fall recovery research.

## Features

* Train reinforcement learning policies for the AiDIN robot using PPO, IsaacGym
* K-ACC clustering for efficient initial state sampling of fallen poses
* CENet (Context Encoder Network) for context-aided fall-recovery learning

## Setup

### Requirements
- Python 3.8
- Isaac Gym v4 (Preview Release)
- PyTorch >= 1.4.0
- NVIDIA GPU with CUDA support

### Installation

```bash
# 1. Create conda environment
conda create -n lifter python=3.8 -y
conda activate lifter

# 2. Install Isaac Gym (download from NVIDIA)
cd /path/to/isaacgym/python
pip install -e .

# 3. Install LIFTER packages
cd /path/to/LIFTER
pip install -e recovery_gym
pip install -e recovery_rl

# 4. Install dependencies
pip install -r requirements.txt
```

## Project Structure

```
LIFTER/
├── accessibility/          # Stage 1: Pose sampling & K-ACC clustering
│   ├── k-access/           # K-means accessibility clustering
│   ├── chord/              # Chord matrix visualization
│   └── tsne/               # t-SNE dimensionality reduction
│
├── recovery_gym/           # Stage 2: Isaac Gym environments
│   ├── envs/               # Environment definitions
│   │   ├── base/           # Base task implementations
│   │   ├── aidin_6/        # 6-leg robot configs
│   │   └── aidin_8/        # 8-leg robot configs
│   ├── scripts/            # Training & evaluation scripts
│   └── utils/              # Terrain, logging utilities
│
├── recovery_rl/            # RL algorithms
│   ├── algorithms/         # PPO, SAC implementations
│   ├── modules/            # Actor-Critic networks
│   ├── runners/            # Training loops
│   └── context/            # CENet encoder
│
├── resources/              # Robot URDF & mesh files
├── scripts/analysis/       # W&B result analysis scripts
└── results/figures/        # Generated plots
```

## Usage

### Stage 1: Sampling Fallen Poses (Accessibility)

Before training, generate diverse fallen poses and compute accessibility metrics using PyBullet simulation.

```bash
cd accessibility/k-access

# Step 1: Sample 3000 random fallen poses
python sample_poses_aidin_8.py      # Generates poses_aidin_8.pickle

# Step 2: Compute accessibility time table (3000x3000 matrix)
# Warning: This step takes a long time due to pairwise pose transition computation
python access_poses_aidin_8.py      # Generates aidin_8_AccessTimeTable.npy

# Step 3: (Optional) Compute Euclidean distance matrix for comparison
python euclidean_poses_aidin_8.py
```

For AiDIN-6 robot, use the corresponding `*_aidin_6.py` scripts.

After generating the accessibility matrix, use the Jupyter notebooks in `accessibility/chord/` to:
- Visualize the accessibility matrix
- Perform K-ACC clustering
- Extract cluster centroids for training initial states

### Stage 2: Training

```bash
python recovery_gym/scripts/train.py --task=aidin_8_stair_rec_context
```

### Evaluation

```bash
python recovery_gym/scripts/play.py --task=aidin_8_stair_rec_context --init_mode=acc
```

### Task Naming Convention

Tasks follow the pattern: `{robot}_{terrain}_{task}_{method}`
- **Robots**: `aidin_6`, `aidin_8`
- **Terrains**: `flat`, `rough`, `stair`
- **Tasks**: `loco` (locomotion), `rec` (recovery)
- **Methods**: `base`, `context` (CENet), `contextlstm`, `oracle`

LIFTER

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