Robot Arm Control via Cognitive Map Learners
Cognitive map learners (CML) have been shown to enable hierarchical, compositional machine learning. That is, interpedently trained CML modules can be arbitrarily composed together to solve more complex problems without task-specific retraining. This work applies this approach to control the movement of a multi-jointed robot arm, whereby each arm segment's angular position is governed by an independently trained CML. Operating in a 2D Cartesian plane, target points are encoded as phasor hypervectors according to fractional power encoding (FPE). This phasor hypervector is then factorized into a set of arm segment angles either via a resonator network or a modern Hopfield network. These arm...
| Authors | Nathan McDonald, Colyn Seeley, Christian Brazeau |
|---|---|
| Primary category | cs.RO |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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