| # DeepReach | |
| ### Researching Data and Orchestration for Real-World Robotics | |
| DeepReach focuses on two tightly coupled research directions: | |
| 1. **Manipulation-Centric Robotic Data** | |
| 2. **DROS — Distributed Robot Operating System** | |
| Our goal is to study how robots learn and coordinate in real production environments. | |
| --- | |
| ## 🧠 Robotic Data | |
| ### Egocentric Manipulation | |
| We collect and structure multi-view, wrist-centered manipulation data for dual-arm systems. | |
| Key properties: | |
| - Egocentric RGB-D streams | |
| - Action-aligned trajectories | |
| - Skill-level segmentation | |
| - Task-sequenced demonstrations | |
| Designed for: | |
| - Imitation learning | |
| - Diffusion-based control policies | |
| - Vision-Language-Action (VLA) models | |
| --- | |
| ### World-Model-Based Annotation | |
| Rather than treating perception as frame-level RGB inputs, we reconstruct structured scene representations: | |
| - Point clouds | |
| - Object-centric embeddings | |
| - Spatial relations | |
| This enables: | |
| - Semantic task querying | |
| - Deployment-time environment reconstruction | |
| - Structured evaluation beyond pixel loss | |
| We view world models as the bridge between perception and manipulation. | |
| --- | |
| ### Manipulation as Compositional Skills | |
| We represent tasks as compositions of atomic skills rather than monolithic policies. | |
| This allows: | |
| - Skill reuse across tasks | |
| - Fine-grained failure analysis | |
| - Scalable dataset construction | |
| --- | |
| ## ⚙️ DROS | |
| ### Distributed Robot Operating System | |
| DROS explores orchestration for heterogeneous robot fleets. | |
| We focus on: | |
| - Capability-aware task decomposition | |
| - Multi-agent coordination under physical constraints | |
| - Integration-aware scheduling across production systems | |
| Rather than optimizing single-agent policies, we study: | |
| > How robotic capabilities compose across agents. | |
| --- | |
| ## 🔁 Closed-Loop Learning | |
| We connect: | |
| Deployment → Data → Model → Evaluation → Redeployment | |
| Robots improve from real-world interaction traces rather than static benchmarks. | |
| --- | |
| ## Research Themes | |
| - Egocentric manipulation learning | |
| - World-model-driven task evaluation | |
| - Multi-agent capability graphs | |
| - Skill composition under uncertainty | |
| - Real-to-real adaptation in production settings | |
| --- | |
| ## Vision | |
| To understand how robotic systems: | |
| - Learn from deployment | |
| - Coordinate across heterogeneous hardware | |
| - Transition from isolated policies to workforce-level intelligence | |
| --- | |
| For collaboration and research inquiries: | |
| contact@deepreach.ai |