Constructing a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac

A hands-on guide to collecting data, training policies, and deploying autonomous medical robotics workflows on real hardware

Table-of-Contents

Introduction

Simulation has been a cornerstone in medical imaging to deal with the info gap. Nonetheless, in healthcare robotics until now, it’s often been too slow, siloed, or difficult to translate into real-world systems.

NVIDIA Isaac for Healthcare, a developer framework for AI healthcare robotics, enables healthcare robotics developers in solving these challenges via offering integrated data collection, training, and evaluation pipelines that work across each simulation and hardware. Specifically, the Isaac for Healthcare v0.4 release provides healthcare developers with an end-to-end SO – ARM based starter workflow and the bring your individual operating room tutorial. The SO-ARM starter workflow lowers the barrier for MedTech developers to experience the complete workflow from simulation to coach to deployment and begin constructing and validating autonomous on real hardware instantly.

On this post, we’ll walk through the starter workflow and its technical implementation details to show you how to construct a surgical assistant robot in less time than ever possible before.

SO-ARM Starter Workflow; Constructing an Embodied Surgical Assistant

The SO-ARM starter workflow introduces a brand new technique to explore surgical assistance tasks, and providing developers with a whole end-to-end pipeline for autonomous surgical assistance:

• Collect real-world and artificial data with SO-ARM using the LeRobot
• Superb-tune GR00t N1.5, evaluate in IsaacLab, then deploy to hardware

This workflow gives developers a protected, repeatable environment to coach and refine assistive skills before moving into the Operating Room.

Technical Implementation

The workflow implements a three-stage pipeline that integrates simulation and real hardware:

1. Data Collection: Mixed simulation and real-world teleoperation demonstrations using using SO101 and LeRobot
2. Model Training: Superb-tuning GR00T N1.5 on combined datasets with dual-camera vision
3. Policy Deployment: Real-time inference on physical hardware with RTI DDS communication

Notably, over 93% of the info used for policy training was generated synthetically in simulation, underscoring the strength of simulation in bridging the robotic data gap.

Sim2Real Mixed Training Approach

The workflow combines simulation and real-world data to deal with the elemental challenge that training robots in the actual world is dear and limited, while pure simulation often fails to capture real-world complexities. The approach uses roughly 70 simulation episodes for diverse scenarios and environmental variations, combined with 10-20 real-world episodes for authenticity and grounding. This mixed training creates policies that generalize beyond either domain alone.

Hardware Requirements

The workflow requires:

• GPU: RT Core-enabled architecture (Ampere or later) with ≥30GB VRAM for GR00TN1.5 inference
• SO-ARM101 Follower: 6-DOF precision manipulator with dual-camera vision (wrist and room). The SO-ARM101 features WOWROBO vision components, including a wrist-mounted camera with a 3D-printed adapter
• SO-ARM101 Leader: 6-DOF Teleoperation interface for expert demonstration collection

Notably, developers could run all of the simulation, training and deployment (3 computers needed for physical AI) on one DGX Spark.

Data Collection Implementation

For real-world data collection with SO-ARM101 hardware or every other version supported in LeRobot:

python lerobot-record  
  --robot.type=so101_follower  
  --robot.port=  
  --robot.cameras="{wrist: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, room: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}}"  
  --robot.id=so101_follower_arm  
  --teleop.type=so101_leader  
  --teleop.port=  
  --teleop.id=so101_leader_arm  
  --dataset.repo_id=/surgical_assistance/surgical_assistance  
  --dataset.num_episodes=15  
  --dataset.single_task="Prepare and hand surgical instruments to surgeon" 

For simulation-based data collection:

python -m simulation.environments.teleoperation_record  
  --enable_cameras  
  --record  
  --dataset_path=/path/to/save/dataset.hdf5  
  --teleop_device=keyboard


python -m simulation.environments.teleoperation_record  
  --port=  
  --enable_cameras  
  --record  
  --dataset_path=/path/to/save/dataset.hdf5 

Simulation Teleoperation Controls

For users without physical SO-ARM101 hardware, the workflow provides keyboard-based teleoperation with the next joint controls:

• Joint 1 (shoulder_pan): Q (+) / U (-)
• Joint 2 (shoulder_lift): W (+) / I (-)
• Joint 3 (elbow_flex): E (+) / O (-)
• Joint 4 (wrist_flex): A (+) / J (-)
• Joint 5 (wrist_roll): S (+) / K (-)
• Joint 6 (gripper): D (+) / L (-)
• R Key: Reset recording environment
• N Key: Mark episode as successful

Model Training Pipeline

After collecting each simulation and real-world data, convert and mix datasets for training:

python -m training.hdf5_to_lerobot  
  --repo_id=surgical_assistance_dataset  
  --hdf5_path=/path/to/your/sim_dataset.hdf5  
  --task_description="Autonomous surgical instrument handling and preparation" 


python -m training.gr00t_n1_5.train  
  --dataset_path /path/to/your/surgical_assistance_dataset  
  --output_dir /path/to/surgical_checkpoints  
  --data_config so100_dualcam 

The trained model processes natural language instructions similar to “Prepare the scalpel for the surgeon” or “Hand me the forceps” and executes the corresponding robotic actions. With LeRobot latest release (0.4.0) you’ll have the option to fine-tune Gr00t N1.5 natively in LeRobot!

End-to-End Sim Collect–Train–Eval Pipelines

Simulation is strongest when it’s a part of a loop: collect → train → evaluate → deploy.

With v0.3, IsaacLab supports this full pipeline:

Generate Synthetic Data in Simulation

• Teleoperate robots using keyboard or hardware controllers
• Capture multi-camera observations, robot states, and actions
• Create diverse datasets with edge cases unattainable to gather safely in real environments

Train and Evaluate Policies

• Deep integration with Isaac Lab’s RL framework for PPO training
• Parallel environments (1000’s of simulations concurrently)
• Built-in trajectory evaluation and success metrics
• Statistical validation across varied scenarios

Convert Models to TensorRT

• Automatic optimization for production deployment
• Support for dynamic shapes and multi-camera inference
• Benchmarking tools to confirm real-time performance

This reduces time from experiment to deployment and makes sim2real a practical a part of day by day development.

Getting Began

Isaac for Healthcare SO-ARM Starter Workflow is offered now. To start:

1. Clone the repository: git clone https://github.com/isaac-for-healthcare/i4h-workflows.git
2. Select a workflow: Start with the SO-ARM Starter Workflow for surgical assistance or explore other workflows
3. Run the setup: Each workflow includes an automatic setup script (e.g., tools/env_setup_so_arm_starter.sh)

Resources