How opengalaxea/galaxeavla works
GalaxeaVLA is a pretrained vision-language-action model for robot control. Its main runtime path turns robot observations and task instructions into batched inference, then postprocessed action chunks for execution; the README describes closed-loop replanning from new observations. The repository also supports demonstration-data loading and processing, action-token representation, model training/fine-tuning, and simulation evaluation. The tree and excerpts establish these subsystems, but do not expose every training or serving call site, so uncertain internal wiring is omitted.
Main entry points: serve_policy.py Policy serving · msgpack.py WebSocket transport · g05 G0.5 policy · processor Sample processors
Components 20
Data pipeline
- LeRobot datasets
src/g05/data - Sample processors
src/g05/data_processor/processor - Data transforms
src/g05/data_processor/transforms
G0.5 model
- ActionCodec tokenizer
src/g05/tokenizer - G0.5 policy
src/g05/models/g05 - Model modules
src/g05/models/g05/model - Qwen3.5 backbone
src/g05/models/g05/qwen35
Inference runtime
- Training entrypoint
scripts/finetune.py - Policy inferencer
src/g05/models/g05/inferencer.py - Policy serving
scripts/serve_policy.py - WebSocket transport
src/g05/utils/websocket/msgpack.py
Robot deployment
- R1 Lite deployment
experiments/r1lite - R1 Pro deployment
experiments/r1pro - SO-100 client
experiments/so100/so100_policy_client.py
Evaluation
- LIBERO evaluation
experiments/libero - RoboTwin evaluation
experiments/robotwin
Other components
- Operator
- Robot demonstrations
- Robot platform
- Simulation environment
Connections 21
- Robot demonstrations → LeRobot datasets: provides episodes
- LeRobot datasets → Sample processors: loads samples
- Data transforms → Sample processors: transforms samples
- Sample processors → Training entrypoint: supplies batches
- Training entrypoint → G0.5 policy: optimizes
- G0.5 policy → Model modules: uses modules
- G0.5 policy → Qwen3.5 backbone: uses backbone
- ActionCodec tokenizer → G0.5 policy: encodes actions
- Operator → Policy serving: starts serving
- Policy serving → Policy inferencer: runs inference
- Policy serving → WebSocket transport: exchanges messages
- R1 Lite deployment → WebSocket transport: requests actions
- R1 Pro deployment → WebSocket transport: requests actions
- SO-100 client → WebSocket transport: requests actions
- WebSocket transport → Policy serving: sends observations
- Policy serving → Policy inferencer: returns actions
- R1 Lite deployment → Robot platform: executes actions
- R1 Pro deployment → Robot platform: executes actions
- SO-100 client → Robot platform: executes actions
- LIBERO evaluation → Simulation environment: evaluates policy
- RoboTwin evaluation → Simulation environment: evaluates policy