Open-source ROS 2 client bridge for pre-motor human intent telemetry (SI Module 01)
Open-source ROS 2 client bridge for pre-motor human intent telemetry (SI Module 01)
Neuropatic-Labs has released an open-source ROS 2 client bridge and technical specification under Apache 2.0 to decode non-contact optical gaze and early reach vectors at 60 Hz using standard camera streams. This tool broadcasts advisory intent directives over local WebSockets, enabling downstream ROS 2 controllers and MoveIt 2 pipelines to pre-align joints and spool actuators before a human hand reaches an exchange boundary. The repository includes a 60 Hz WebSocket JSON telemetry schema, a synthetic telemetry generator for local testing, a reference ROS 2 subscriber with a 33.3 ms watchdog bridge node, and a technical white paper detailing the kinematic model. While the core edge inference container remains proprietary for on-premise evaluation pilots, the client bridge and synthetic server can be run locally against existing ROS 2 workspaces.
The release provides an open-source client bridge and technical specification for decoding pre-motor human intent via optical gaze and reach vectors at 60 Hz to enable actuator pre-alignment in collaborative robotics, while the core edge inference container remains proprietary for on-premise evaluation pilots.
Source: ROS Discourse — Community updates · Read original article ↗
Article text · Original source · English
When collaborative robots interact with people in handoff or shared-workspace tasks, motion planners frequently encounter an actuator latency penalty: by the time a human hand breaches a reactive tripwire or spatial bounding box, physical motors and joint gearboxes take 150–300 ms to spool up from a dead stop, resulting in perceptible hesitation.
Human movement exhibits an anticipatory lead time. Research in human factors and visual perception shows that ocular fixation and early reach kinematics lock onto target zones roughly 200–300 ms before physical contact.
SI Module 01 decodes non-contact optical gaze and early reach vectors at the edge using standard camera streams, broadcasting 60 Hz advisory intent directives over local WebSockets. This enables downstream ROS 2 controllers and MoveIt 2 pipelines to pre-align joints and pre-spool actuators before the human hand reaches the exchange boundary.
To allow the robotics community to evaluate the telemetry architecture and wire contract, the client integration bridge and technical specification have been released under Apache 2.0:
GitHub Repository: GitHub - Neuropatic-Labs/si-module-01-bridge: Client integration bridge, 60Hz telemetry schema, and ROS 2 adapters for SI Module 01 pre-motor intent engine. · GitHub
The open-source repository includes:
• 60 Hz WebSocket JSON telemetry schema (telemetry.v1.json)
• Synthetic 60 Hz telemetry generator for local testing (mock_telemetry_server.py)
• Reference ROS 2 subscriber and 33.3 ms watchdog bridge node (si_module_01_bridge.py)
• Technical white paper detailing the kinematic model and systems architecture (The Architecture of Intent)
While our core edge inference container (si-module-01:rc1) remains proprietary for on-premise evaluation pilots, the client bridge and synthetic server can be run locally against your existing ROS 2 workspace in a few commands:
git clone [https://github.com/Neuropatic-Labs/si-module-01-bridge.git](https://github.com/Neuropatic-Labs/si-module-01-bridge.git)
cd si-module-01-bridge
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python3 -m bridge.mock_telemetry_server
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
actuator latency penalty
150–300
View original evidence
When collaborative robots interact with people in handoff or shared-workspace tasks, motion planners frequently encounter an actuator latency penalty: by the time a human hand breaches a reactive tripwire or spatial bounding box, physical motors and joint gearboxes take 150–300 ms to spool up from a dead stop, resulting in perceptible hesitation. Human movement exhibits an anticipatory lead time. Research in human fa
Open source S2ocular fixation and early reach kinematics lead time
200–300
View original evidence
ctors and visual perception shows that ocular fixation and early reach kinematics lock onto target zones roughly 200–300 ms before physical contact. SI Module 01 decodes non-contact optical gaze and early reach vectors at the edge using standard camera streams, broadcasting 60 Hz advisory intent directives over local WebSockets. This enables downstream ROS 2 controllers and MoveIt 2 pipelines to pre-align joints and
Open source S3telemetry frequency
60
View original evidence
ctors and visual perception shows that ocular fixation and early reach kinematics lock onto target zones roughly 200–300 ms before physical contact. SI Module 01 decodes non-contact optical gaze and early reach vectors at the edge using standard camera streams, broadcasting 60 Hz advisory intent directives over local WebSockets. This enables downstream ROS 2 controllers and MoveIt 2 pipelines to pre-align joints and
Open source S3watchdog bridge node latency
33.3
View original evidence
ntent engine. · GitHub The open-source repository includes: • 60 Hz WebSocket JSON telemetry schema ( telemetry.v1.json ) • Synthetic 60 Hz telemetry generator for local testing ( mock_telemetry_server.py ) • Reference ROS 2 subscriber and 33.3 ms watchdog bridge node ( si_module_01_bridge.py ) • Technical white paper detailing the kinematic model and systems architecture ( The Architecture of Intent ) While our core
Open source S5
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
pre-spool actuators before the human hand reaches the exchange boundary. To allow the robotics community to evaluate the telemetry architecture and wire contract, the client integration bridge and technical specification have been released under Apache 2.0: GitHub Repository: GitHub - Neuropatic-Labs/si-module-01-bridge: Client integration bridge, 60Hz telemetry schema, and ROS 2 adapters for SI Module 01 pre-motor i
Open source S4
When collaborative robots interact with people in handoff or shared-workspace tasks, motion planners frequently encounter an actuator latency penalty: by the time a human hand breaches a reactive tripwire or spatial bounding box, physical motors and joint gearboxes take 150–300 ms to spool up from a dead stop, resulting in perceptible hesitation. Human movement exhibits an anticipatory lead time. Research in human fa
Open source S2
ctors and visual perception shows that ocular fixation and early reach kinematics lock onto target zones roughly 200–300 ms before physical contact. SI Module 01 decodes non-contact optical gaze and early reach vectors at the edge using standard camera streams, broadcasting 60 Hz advisory intent directives over local WebSockets. This enables downstream ROS 2 controllers and MoveIt 2 pipelines to pre-align joints and
Open source S3
ntent engine. · GitHub The open-source repository includes: • 60 Hz WebSocket JSON telemetry schema ( telemetry.v1.json ) • Synthetic 60 Hz telemetry generator for local testing ( mock_telemetry_server.py ) • Reference ROS 2 subscriber and 33.3 ms watchdog bridge node ( si_module_01_bridge.py ) • Technical white paper detailing the kinematic model and systems architecture ( The Architecture of Intent ) While our core
Open source S5
edge inference container ( si-module-01:rc1 ) remains proprietary for on-premise evaluation pilots, the client bridge and synthetic server can be run locally against your existing ROS 2 workspace in a few commands: git clone [https://github.com/Neuropatic-Labs/si-module-01-bridge.git](https://github.com/Neuropatic-Labs/si-module-01-bridge.git) cd si-module-01-bridge python3 -m venv .venv source .venv/bin/activate pi
Open source S6
Source：ROS Discourse — Community updates · discourse.openrobotics.org