Knowledge-Guided Manipulation Using Multi-Task Reinforcement Learning
This paper introduces Knowledge Graph based Massively Multi-task Model-based Policy Optimization (KG-M3PO), a framework for multi-task robotic manipulation in partially observable settings that unifies Perception, Knowledge, and Policy. The method augments egocentric vision with an online 3D scene graph that grounds open-vocabulary detections into a metric, relational representation. A dynamic-relation mechanism updates spatial, containment, and affordance edges at every step, and a graph neural encoder is trained end-to-end through the RL objective so that relational features are shaped directly by control performance. Multiple observation modalities (visual, proprioceptive, linguistic, ...
| Authors | Aditya Narendra, Mukhammadrizo Maribjonov, Dmitry Makarov et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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