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Shannon Sands
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@ -2271,6 +2271,188 @@ config = SanskritPoetryEnvConfig(
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---
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### 26. OpenVLA Robotics Environment (`openvla_robotics/`)
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**Contributors**: RahulSChand
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**PR**: [#65](https://github.com/NousResearch/atropos/pull/65)
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**Integration Status**: ✅ Integrated
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**Description**: A robotics reinforcement learning environment that integrates OpenVLA (Vision-Language-Action) models with robosuite simulation for training embodied AI agents. This environment enables language models to learn robotic manipulation tasks through vision-based action prediction and continuous control.
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**Core Features**:
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**OpenVLA Integration**:
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- **Vision-Language-Action Model**: Uses OpenVLA-7B for multimodal robot control
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- **Visual Input Processing**: Processes camera observations from robosuite environments
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- **Action Prediction**: Generates continuous robot actions from visual and language inputs
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- **Robosuite Simulation**: Integrates with robosuite for realistic robot simulation
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**Robotics Simulation**:
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- **Robosuite Environment**: Configurable robot tasks (Lift, NutAssemblySquare, etc.)
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- **Panda Robot**: Simulated Franka Emika Panda robot arm
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- **Camera Observations**: Front-view camera with 640x480 resolution
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- **Continuous Control**: 7-DOF action space for robot manipulation
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**Action Tokenization**:
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- **Continuous to Discrete**: Custom action tokenizer for converting continuous actions to tokens
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- **Uniform Binning**: 256 bins per action dimension with configurable ranges
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- **Token Mapping**: Maps actions to least-used vocabulary tokens
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- **Bidirectional Conversion**: Encode actions to tokens and decode back to continuous values
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**Training Architecture**:
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- **Vision-Language Input**: Processes camera images with text prompts
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- **Action Generation**: Predicts robot actions using OpenVLA model
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- **Reward Collection**: Gathers rewards from robosuite environment
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- **Trajectory Scoring**: Scores action sequences based on task performance
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**Technical Implementation**:
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**Model Configuration**:
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- **OpenVLA Model**: `openvla/openvla-7b` with bfloat16 precision
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- **GPU Acceleration**: CUDA support for model inference
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- **Vision Processing**: AutoProcessor for image and text input handling
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- **Action Space**: 7-dimensional continuous action space (position + gripper)
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**Environment Setup**:
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```python
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# Robosuite environment configuration
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self.robosuite_env = suite.make(
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"Lift", # Task: pick up cube
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robots="Panda", # Franka Emika Panda arm
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has_renderer=False, # Headless simulation
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has_offscreen_renderer=True, # Camera rendering
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use_camera_obs=True, # Visual observations
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camera_names="frontview", # Front camera
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camera_heights=640, # Image height
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camera_widths=480 # Image width
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)
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```
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**Action Processing Pipeline**:
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1. **Visual Observation**: Extract camera image from robosuite
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2. **Prompt Construction**: Create task-specific text prompt
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3. **Model Inference**: Generate action using OpenVLA model
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4. **Action Adjustment**: Scale and transform actions for robosuite
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5. **Environment Step**: Execute action in simulation
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6. **Reward Collection**: Gather task performance feedback
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**Action Tokenizer Features**:
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- **Discretization**: Converts continuous actions to discrete tokens
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- **Vocabulary Mapping**: Uses least-frequent tokens for action representation
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- **Configurable Binning**: Adjustable number of bins and action ranges
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- **Efficient Encoding**: Minimal vocabulary overhead for action space
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**Research Applications**:
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**Embodied AI**:
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- **Vision-Language-Action Learning**: Training models to understand and act in physical environments
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- **Multimodal Control**: Combining visual perception with language understanding for robot control
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- **Sim-to-Real Transfer**: Foundation for transferring learned policies to real robots
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- **Task Generalization**: Learning manipulation skills across different robotic tasks
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**Robotics Research**:
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- **Manipulation Learning**: Training robots to perform complex manipulation tasks
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- **Visual Servoing**: Learning to control robots based on visual feedback
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- **Language-Conditioned Control**: Following natural language instructions for robot tasks
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- **Continuous Control**: Learning smooth, continuous robot motions
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**Technical Challenges Addressed**:
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- **Action Space Discretization**: Converting continuous robot actions to discrete tokens
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- **Vision-Language Integration**: Combining visual and linguistic information for control
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- **Simulation Integration**: Bridging language models with physics simulation
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- **Real-time Control**: Generating robot actions at appropriate frequencies
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**Current Implementation Status**:
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- **Prototype Stage**: Basic integration with OpenVLA and robosuite
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- **Single Task**: Currently configured for cube lifting task
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- **Development Mode**: Includes TODO comments for future enhancements
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- **GPU Required**: Requires CUDA-capable GPU for OpenVLA inference
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**Configuration Options**:
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- **Robot Tasks**: Configurable robosuite environments (Lift, NutAssemblySquare, etc.)
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- **Action Binning**: Adjustable discretization parameters (bins, ranges)
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- **Model Settings**: Configurable OpenVLA model parameters
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- **Simulation Parameters**: Camera settings, rendering options, robot configuration
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**Future Enhancements**:
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**Multi-Task Learning**:
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- **Task Variety**: Support for multiple robosuite manipulation tasks
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- **Task Conditioning**: Language-conditioned task specification
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- **Curriculum Learning**: Progressive difficulty in manipulation tasks
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- **Transfer Learning**: Knowledge sharing across different robot tasks
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**Advanced Features**:
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- **Multi-Step Planning**: Long-horizon task planning and execution
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- **Error Recovery**: Robust handling of action failures and retries
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- **Real Robot Integration**: Extension to physical robot platforms
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- **Human Demonstrations**: Integration of human demonstration data
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**Performance Optimization**:
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- **Batch Processing**: Parallel trajectory collection for efficiency
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- **Model Optimization**: Quantization and acceleration for faster inference
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- **Memory Management**: Efficient handling of visual observations
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- **Distributed Training**: Multi-GPU and multi-node training support
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**Setup Requirements**:
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**Hardware**:
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- **GPU**: CUDA-capable GPU with sufficient VRAM (8GB+ recommended)
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- **Memory**: 16GB+ RAM for model loading and simulation
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- **Storage**: Space for OpenVLA model weights (~14GB)
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**Software Dependencies**:
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```bash
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# Core robotics and ML libraries
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pip install robosuite torch transformers pillow
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# OpenVLA model (requires trust_remote_code=True)
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# Model will be downloaded automatically on first run
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```
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**Installation & Usage**:
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```bash
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# Navigate to environment directory
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cd environments/community/openvla_robotics/
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# Run the robotics environment
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python open_robot_env.py
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# Note: First run will download OpenVLA-7B model (~14GB)
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```
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**Example Training Flow**:
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```python
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# Initialize environment
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env = RobotSimEnv(config, server_configs)
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await env.setup()
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# Training loop
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for episode in range(num_episodes):
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# Get next training item (resets environment)
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item = await env.get_next_item()
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# Collect robot trajectory
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scored_data, backlog = await env.collect_trajectories(item)
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# Process rewards and update policy
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# (Policy update logic would be implemented here)
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```
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**Research Impact**: This environment represents an important step toward training language models for embodied AI tasks. By combining OpenVLA's vision-language-action capabilities with robosuite's realistic simulation, it provides a foundation for developing robots that can understand and execute complex manipulation tasks based on natural language instructions.
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**Educational Value**: The environment demonstrates the integration of multiple complex systems (vision-language models, robotics simulation, action tokenization) and serves as a practical example of how modern AI techniques can be applied to robotics challenges.
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**Limitations**:
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- **Single Task Focus**: Currently limited to cube lifting task
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- **Prototype Implementation**: Contains placeholder code and TODO items
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- **GPU Dependency**: Requires significant computational resources
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- **No Evaluation Data**: Lacks standardized evaluation benchmarks
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**Requirements**: robosuite, torch, transformers, pillow, atroposlib
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---
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## Support
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For questions or issues with community environments:
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