A robot controller written directly into neural-network weights, in ROS 2 and Gazebo
Overview
The author implements a reactive navigation controller using a small multi-layer perceptron with directly authored weights in ROS 2 and Gazebo. The system achieves doorway crossing and completes three laps without gradient-based training or a full navigation stack. The implementation includes recorded trajectories, failures, and a configured Nav2 reference for context, currently limited to simulation.
Generated from attributed reports · Updated 2 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · stored coefficients and biases: 738 other · Basis not reported
“on. Recorded Gazebo trajectory at 16× playback. A separate pose-based scheduler advances the three-lap mission. The network is a 26 → 16 → 16 → 2 tanh MLP : 738 stored coefficients and biases, 78 nonzero in the final version. It takes reduced lidar readings and produces speed and steering signals, with no map, path planner or LLM call in the steering loop. navigation-stack-vs-neural-policy 1600×1430 159 KB The crosse”
Exact source · revision 2Source owner not reported
Reported quantity · nonzero coefficients and biases: 78 other · Basis not reported
“on. Recorded Gazebo trajectory at 16× playback. A separate pose-based scheduler advances the three-lap mission. The network is a 26 → 16 → 16 → 2 tanh MLP : 738 stored coefficients and biases, 78 nonzero in the final version. It takes reduced lidar readings and produces speed and steering signals, with no map, path planner or LLM call in the steering loop. navigation-stack-vs-neural-policy 1600×1430 159 KB The crosse”
Exact source · revision 2Source owner not reported
Reported quantity · laps: 3 other · reported trials
“rms still dominated the turn. The activation trace pointed to the steering priority that needed changing. After feedback-guided revisions, the controller crossed the doorway from several starts. With an external lane scheduler, it also completed three laps through both rooms. Scan processing, command safeguards and that scheduler remain ordinary code outside the network. There was no gradient-based training, but ther”
Exact source · revision 2Source owner not reported
Artifact availability · code: reported pending
“e was iteration; the longer mission uses Gazebo true pose for lap closure. I’ve published the implementation, recorded trajectories and failures, plus a configured Nav2 reference for context. For now, this is simulation only. Over the next few days I’ll put it on the actual dum-e and see what happens. Suggestions, opinions and criticism are all welcome! Article, replay and method · Code and evidence”
Exact source · revision 2Source owner not reported
Dataset · modalities: lidar
“on. Recorded Gazebo trajectory at 16× playback. A separate pose-based scheduler advances the three-lap mission. The network is a 26 → 16 → 16 → 2 tanh MLP : 738 stored coefficients and biases, 78 nonzero in the final version. It takes reduced lidar readings and produces speed and steering signals, with no map, path planner or LLM call in the steering loop. navigation-stack-vs-neural-policy 1600×1430 159 KB The crosse”
Exact source · revision 2Source owner not reported
Dataset · tasks: navigation
“After reading Sparks of Weight-Space Programming by Rikiya Takehi, Jayden Teoh, Alex Zhang and Omar Khattab, I wanted to try the idea on a robotics problem: could directly authored neural-network weights produce useful navigation behavior? I set up two rooms, a doorway and a lidar-equipped robot in Gazebo. Fable 5.1 wrote and revised the weights of a small network; a ROS 2 node ran it and turned its outputs into moti”
Exact source · revision 2Source owner not reported
Dataset · embodiments: dum-e
“e was iteration; the longer mission uses Gazebo true pose for lap closure. I’ve published the implementation, recorded trajectories and failures, plus a configured Nav2 reference for context. For now, this is simulation only. Over the next few days I’ll put it on the actual dum-e and see what happens. Suggestions, opinions and criticism are all welcome! Article, replay and method · Code and evidence”
Exact source · revision 2Source owner not reported
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The author implements a reactive navigation controller using a small multi-layer perceptron with directly authored weights in ROS 2 and Gazebo. The system achieves doorway crossing and completes three laps without gradient-based training or a full navigation stack. The implementation includes recorded trajectories, failures, and a configured Nav2 reference for context, currently limited to simulation.
Event coverage history
Current attention 3·Peak within the comparable range 10（2026-11-07 22:00 UTC）·Change within the comparable range over 24 hours -49%
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