SPACECAT

VLA Fine-tuning Module Update A. Fine-Tuning Pipeline Enhancements - Added default configurations and state management for multiple optimizers, including SGD, SGD with Momentum, Adam, and AdamW, enabling end-users to experiment with different optimization strategies. github.com/RoboraDev/VLA_Mod… - Integrated several learning rate scheduler wrappers -> StepLR, CosineAnnealing, LinearDecay, and ExponentialDecay, allowing seamless selection and comparison through Wandb for fine-tuning analytics. github.com/RoboraDev/VLA_Mod… - Default implementations for Pi0, Pi0.5, and SmolVLA support a maximum action and observation dimension of 32. For high-complexity robotic agents such as Humanoid, the action encoder, state encoder, and action decoder feature dimensions will need to be scaled accordingly, these can be done easily as these are 3 lines of pytorch code but have to be implemented from scratch. For each model, this will be specified inside config.py and the corresponding WithExpert.py file will inherit it. github.com/RoboraDev/VLA_Mod… B. PI0 Policy Implementation - Implemented the Pi0 Policy architecture in PyTorch, referencing the Physical Intelligence OpenPI repository for core design principles & configurations. - Implemented PI0Config, will also be added yaml, json support for custom configs in fine-tuning. github.com/RoboraDev/VLA_Mod… C. VLA Util helper functions Implementation - get_device_info, torch_device, device_name for accelerators - JSON Serialize & deserialize functions to store the optimizers state on disk. - Parameters utility helpers : get device, dtype, output shape etc github.com/RoboraDev/VLA_Mod… Plan for Tomorrow: - Support for SmolVLA and Pi0.5 will be implemented next, as these architectures share a common Vision-Language Model (VLM) architecture with minor variations in module structure and feature dimensions. - Lerobot dataset framework integration for dataset management: will integrate online dataset as a Proof-of-concept.

Real-world data is the missing link between simulation and physical intelligence. With the Lemorele P300 (R), Robora is closing that gap, turning every user into a contributor to embodied AI evolution. It enables real-time, high-definition video capture from any camera, whether it’s mounted on a robot prototype, a drone, or a handheld camera. This visual feed is transmitted wirelessly and losslessly to a connected device (tablet, phone, or PC) that runs the Robora VLA interface or data capture app. The device then streams or uploads these feeds directly into Robora’s cloud or local VLA processing node. By collecting data from diverse environments and use cases, Robora gains the foundation to train and fine-tune its models on real-world sensory information, moving beyond purely simulated data. This approach is key to reducing the sim-to-real gap, the performance difference between robots trained in simulation and those operating in complex physical environments.