real-stanford/diffusion_policy

Features

• Diffusion Policy Learning - Uses diffusion-based neural networks to model complex action distributions for stable and flexible robotic behavior.
• Robotic Policy Learning - Uses diffusion models to map visual observations to precise action trajectories for robotic control.
• Robotic Behavior Execution - Translates AI inference results into timed sequences of physical robotic behaviors while maintaining high-frequency observation loops.
• Diffusion Conditioning Architectures - Uses conditional denoising diffusion architectures to map visual observations to precise robotic action sequences.
• Diffusion Policy Training - Trains neural networks mapping visual observations to robotic actions using a diffusion-based approach.
• Score-Based Action Models - Learns the gradient of the action distribution to represent multiple valid movement trajectories for a single state.
• Diffusion Model Frameworks - Provides a specialized framework for training and sampling from diffusion models to generate robotic action sequences.
• Imitation and Reinforcement Learning Toolkits - Ships a comprehensive toolkit for collecting human demonstrations and training robot policies via imitation learning.
• Imitation Learning Trainings - Trains robot policies from demonstration data to map camera images to precise action trajectories.
• Robot Action Model Frameworks - Implements a system for training and deploying neural networks that generate precise robotic control signals.
• Robotic Control Policies - Maps sensory visual inputs to action tokens to achieve precise visuomotor control for complex physical tasks.
• Action Trajectory Sampling - Performs intricate physical maneuvers by sampling action trajectories from a learned diffusion process.
• Multimodal Action Prediction - Handles multimodal action distributions to manage multiple valid robotic trajectories for a single state.
• Receding Horizon Control - Optimizes action sequences over a sliding time window during inference to maintain physical stability.
• Robotic Policy Evaluators - Provides tools for executing benchmarks and logging performance metrics on physical and simulated robotic hardware.
• Robust Visuomotor Policies - Executes learned tasks while resisting external disruptions such as visual occlusions or physical interference.
• Data Pipeline Configurations - Provides configuration schemas to define how multimodal sensor and image data are batched and shuffled for training.
• Trajectory Data Pipelines - Provides a data pipeline for standardizing and managing high-frequency robotic trajectory datasets.
• Shared Memory Buffers - Employs shared-memory buffering to capture high-frequency camera data and reduce processing latency.
• Chunked File Storages - Implements a compressed, chunked storage system for handling large-scale robotic demonstration datasets efficiently on disk.
• Telemetry Data Pipelines - Provides a telemetry data pipeline for batching and streaming high-resolution image and sensor data for training.
• Demonstration Collection Systems - Ships a system for capturing high-quality human-led manipulation data via teleoperation to create training datasets.
• Shared Memory Buffers - Captures high-frequency camera data into shared memory to minimize processing delays and serialization overhead.
• Sliding Window Optimizations - Utilizes sliding-window numerical optimizations to maintain stability and consistency during physical robot movements.
• Trajectory Experience Storage - Implements a storage system for recording and indexing large-scale robotic state-action trajectories.

Star history