srv-d7aoqmh5pdvs7391dcqg
MCP ServerFree# NWO Robotics MCP Server Control real robots, IoT devices, and autonomous agent swarms through natural language — powered by the [NWO Robotics API](https://nwo.capital). --- ## What This Server Does This MCP server exposes the full NWO Robotics API as 64 ready-to-use tools. Any MCP-compatible A
Capabilities5 decomposed
natural language robot control
Medium confidenceThis capability allows users to send natural language commands to control physical robots, utilizing the NWO Robotics API to interpret and execute these commands. The system employs advanced NLP techniques to parse user instructions and translate them into actionable commands for the robots, ensuring seamless interaction without requiring programming knowledge. This is distinct due to its integration with real-time sensor data for context-aware actions.
Utilizes a natural language processing engine specifically tuned for robotic commands, allowing for intuitive user interactions without technical jargon.
More user-friendly than traditional command-line interfaces, enabling non-technical users to control robots effectively.
real-time vla inference
Medium confidenceThis capability runs Vision-Language-Action (VLA) inference by combining text instructions with live camera feeds, producing joint action vectors in real time. It leverages edge computing via Cloudflare to minimize latency, achieving an average response time of 28ms. The system supports auto model routing to select the best model for the task dynamically, enhancing performance and accuracy.
Employs ultra-low-latency edge inference to deliver real-time responses, making it suitable for dynamic environments where speed is critical.
Faster and more responsive than traditional cloud-based VLA systems, which can suffer from higher latency.
multi-step task planning
Medium confidenceThis capability decomposes complex tasks into manageable subtasks, allowing robots to execute them step-by-step. It uses a task planner that logs outcomes and learns from each execution to improve future performance. The system polls progress and validates each step, ensuring that tasks are completed efficiently and accurately.
Incorporates a feedback loop for continuous learning from task execution, enhancing the robot's ability to handle similar tasks in the future.
More adaptive than static task execution systems, as it learns from past experiences to optimize future tasks.
sensor fusion for robot state
Medium confidenceThis capability allows for querying and integrating data from multiple sensors (camera, lidar, thermal, etc.) to provide a comprehensive view of the robot's state. It fuses this data into a single inference call, enabling more informed decision-making and action execution. The integration of various sensor modalities enhances the robot's situational awareness.
Utilizes a sophisticated fusion algorithm to combine data from diverse sensor types, providing a richer context for robot operations.
More comprehensive than single-sensor systems, which can miss critical information due to lack of context.
online reinforcement learning
Medium confidenceThis capability enables the initiation of online reinforcement learning sessions, where robots can learn from their actions in real-time. It streams telemetry data (state, action, reward) back to the server, allowing for the creation of fine-tuning datasets from logged runs. This process supports continuous improvement of the robot's performance through iterative learning.
Offers a streamlined process for real-time learning and adaptation, allowing robots to improve their capabilities dynamically based on their experiences.
More efficient than traditional batch learning approaches, which can be slower and less responsive to changing environments.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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RT-1: Robotics Transformer for Real-World Control at Scale (RT-1)
## Historical Papers <a name="history"></a>
Symbolic Discovery of Optimization Algorithms (Lion)
* ⭐ 07/2023: [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (RT-2)](https://arxiv.org/abs/2307.15818)
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RT-2
Google's vision-language-action model for robotics.
LiteWebAgent
[NAACL2025] LiteWebAgent: The Open-Source Suite for VLM-Based Web-Agent Applications
LiquidAI: LFM2-24B-A2B
LFM2-24B-A2B is the largest model in the LFM2 family of hybrid architectures designed for efficient on-device deployment. Built as a 24B parameter Mixture-of-Experts model with only 2B active parameters per...
Best For
- ✓robotics developers looking to simplify robot control
- ✓developers building interactive robotics applications
- ✓robotics engineers designing complex workflows
- ✓developers working on advanced robotic systems
- ✓researchers and developers focusing on AI training
Known Limitations
- ⚠Limited to predefined command structures; complex tasks may require additional programming.
- ⚠Dependent on camera quality and environmental conditions for accurate inference.
- ⚠Requires well-defined tasks; ambiguity can lead to execution errors.
- ⚠Sensor compatibility may vary; not all sensors are supported.
- ⚠Requires substantial computational resources for complex learning tasks.
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
# NWO Robotics MCP Server Control real robots, IoT devices, and autonomous agent swarms through natural language — powered by the [NWO Robotics API](https://nwo.capital). --- ## What This Server Does This MCP server exposes the full NWO Robotics API as 64 ready-to-use tools. Any MCP-compatible AI agent (Claude, ChatGPT, Cursor, etc.) can use it to: - Send natural language instructions to physical robots - Run Visual-Language-Action (VLA) inference on live camera feeds - Plan, validate, and execute multi-step robot tasks - Monitor sensors, detect slip, and fuse multi-modal data - Train robots online with reinforcement learning - Register and manage agent identities on Base mainnet via the Cardiac biometric ID system No local installation needed. The server runs on Render and is ready to connect. --- ## Tools Overview ### 🤖 VLA Inference & Models Run Vision-Language-Action inference on any supported robot. Send a text instruction and camera images, receive joint action vectors in real time. Supports auto model routing, ultra-low-latency Cloudflare edge inference (28ms avg), and WebSocket streaming at up to 50Hz. `vla_inference` · `edge_inference` · `list_models` · `get_model_info` · `get_streaming_config` --- ### 🦾 Robot Control & State Query live robot state (joint angles, gripper, battery, position), execute pre-computed action sequences, and fuse camera + lidar + thermal + force + GPS sensor inputs into a single inference call. `query_robot_state` · `execute_actions` · `sensor_fusion` · `robot_query` · `get_agent_status` --- ### 🗺️ Task Planning & Learning Decompose complex instructions into ordered subtasks, execute them step by step, poll progress, and log outcomes so the model learns and improves with every run. `task_planner` · `execute_subtask` · `status_poll` · `learning_recommend` · `learning_log` --- ### 🔑 Agent Management Self-register a new AI agent in under 2 seconds, check your monthly API quota, upgrade tiers by paying ETH, and manage robot registrations and capabilities. | Tier | Calls/month | Cost | |------|-------------|------| | Free | 100,000 | $0 | | Prototype | 500,000 | ~0.015 ETH/mo | | Production | Unlimited | ~0.062 ETH/mo | `register_agent` · `check_balance` · `pay_upgrade` · `create_wallet` · `register_robot` · `update_agent` · `get_agent_info` --- ### 🔍 Agent Discovery Discover all available execution modes (mock / simulated / live), robot types, VLA models, and sensor capabilities. Validate tasks with a dry-run before committing to execution. `nwo_health` · `nwo_whoami` · `discover_capabilities` · `dry_run` · `plan_task` --- ### 🔌 ROS2 Bridge (Physical Robots) Connect directly to physical robots over the ROS2 bridge. Send joint commands, submit action sequences, and trigger emergency stops on one or all robots within 10ms. Supported: UR5e, Panda, Spot, Unitree G1, and more. `ros2_list_robots` · `ros2_robot_status` · `ros2_send_command` · `ros2_submit_action` · `ros2_emergency_stop` · `ros2_emergency_stop_all` · `ros2_get_robot_types` --- ### 🧪 Physics Simulation Simulate trajectories, check for collisions, estimate joint torques, validate grasps, and plan collision-free motions with MoveIt2 — before touching real hardware. `simulate_trajectory` · `check_collision` · `estimate_torques` · `validate_grasp` · `plan_motion` · `get_scene_library` · `generate_scene` --- ### 📐 Embodiment & Calibration Browse the robot embodiment registry (DOF, joint limits, sensors), download URDF models, get normalization parameters for VLA inference, and run automatic joint calibration. `list_embodiments` · `get_robot_specs` · `get_normalization` · `download_urdf` · `get_test_results` · `compare_robots` · `run_calibration` · `calibrate_confidence` --- ### 🧠 Online RL & Fine-Tuning Start online reinforcement learning sessions, stream state/action/reward telemetry, build fine-tuning datasets from logged runs, and launch LoRA fine-tuning jobs on any base VLA model. `start_rl_training` · `submit_rl_telemetry` · `create_finetune_dataset` · `start_finetune_job` --- ### 🖐️ Tactile Sensing (ORCA Hand) Read 256-taxel tactile sensor arrays from the ORCA robot hand, assess grip quality and object texture, and detect slip in real time to prevent dropped objects. `read_tactile` · `process_tactile` · `detect_slip` --- ### 📦 Dataset Hub Access 1.54 million+ human robot demonstrations for the Unitree G1 humanoid (430+ hours, LeRobot-compatible format) for training and fine-tuning. `list_datasets` --- ### 🫀 Cardiac Blockchain Identity (Base Mainnet) Register AI agents on Base mainnet and receive a permanent soul-bound Digital ID (`rootTokenId`). Issue verifiable credentials for task authorization, swarm control, location access, and payments — all gasless via the NWO relayer. Smart contracts deployed on Base Mainnet (Chain ID 8453): - `NWOIdentityRegistry` — `0x78455AFd5E5088F8B5fecA0523291A75De1dAfF8` - `NWOAccessController` — `0x29d177bedaef29304eacdc63b2d0285c459a0f50` - `NWOPaymentProcessor` — `0x4afa4618bb992a073dbcfbddd6d1aebc3d5abd7c` `cardiac_register_agent` · `cardiac_identify_agent` · `cardiac_renew_key` · `cardiac_issue_credential` · `cardiac_check_credential` · `cardiac_grant_access` · `cardiac_get_nonce` · `cardiac_check_access` · `cardiac_payment_process` --- ### 🔮 Cardiac Oracle Validate ECG biometric data from smartwatches to authenticate human identities, compute cardiac hashes, and verify recent validations. `oracle_health` · `oracle_validate_ecg` · `oracle_hash_ecg` · `oracle_verify` --- ## Supported Robot Models | Model | Type | Capabilities | |-------|------|--------------| | `xiaomi-robotics-0` | VLA | Grasp, navigate, manipulate | | `pi05` | VLA | General manipulation | | `groot_n1.7` | VLA | Humanoid control | | `deepseek-ocr-2b` | OCR | Label reading, text recognition | --- ## Example Usage **Pick and place:** > "Pick up the red box from the table and place it on shelf B" **Sensor query:** > "What is the temperature in warehouse zone 3?" **Safety:** > "Run a safety check before moving robot_001 to the loading dock" **Swarm:** > "Deploy all available robots to patrol the perimeter" **Learning:** > "What grip technique should I use for fragile glass objects?" --- ## Links - 🌐 [NWO Capital](https://nwo.capital) - 📄 [Agent Skill File](https://nwo.capital/webapp/agent.md) - 📖 [API Docs](https://nwo.capital/webapp/nwo-robotics.html) - 🧬 [Cardiac SDK](https://github.com/RedCiprianPater/nwo-cardiac-sdk) - 🔑 [Get API Key](https://nwo.capital/webapp/api-key.php) - 🤗 [Live Demo](https://huggingface.co/spaces/PUBLICAE/nwo-robotics-api-demo) - 📜 [OpenAPI Spec](https://nwo.capital/openapi.yaml) --- ## Support 📧 support@nwo.capital
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