Explore Expanded E2E Workloads for Robotics

[cagataycali]

Description

Explore more End-to-End (E2E) workloads for Robotics, such as:

• GR00T Fine-Tuning
• Evaluation workflows
• Reinforcement Learning (RL)
• Synthetic Data Generation (SDG)

Scope

This is a broader initiative to expand beyond inference-only workflows.

Proposed Workloads

1. GR00T Fine-Tuning

• Custom dataset fine-tuning
• Transfer learning workflows
• Model adaptation for specific tasks

2. Evaluation Workflows

• Benchmark suites
• Performance metrics collection
• Automated evaluation pipelines

3. Reinforcement Learning (RL)

• RL training integration
• Policy optimization
• Sim-to-real transfer

4. Synthetic Data Generation (SDG)

• Automated data generation
• Domain randomization
• Data augmentation pipelines

Acceptance Criteria

• Research and document each workload type
• Prioritize workloads based on customer needs
• Create separate issues for each workload
• Design architecture for multi-workload support
• Prototype at least one workload

Priority

Low - Future enhancement