3 accepted papers
This paper introduces Knowledge-Guided 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. KG-M3PO leverages a model-based policy optimization method to contro…
The paper presents a new approach for constructing a library of optimal trajectories for two robotic manipulators, Two-Arm Optimal Control and Avoidance Library (TOCALib). The optimization takes into account kinodynamic and other constraints within the FROST framework. The novelty of the method lies…
We introduce Massively Multi-Task Model-Based Policy Optimization (M3PO), a scalable model-based reinforcement learning (MBRL) framework designed to address the challenges of sample efficiency in single-task settings and generalization in multi-task domains. Existing model-based approaches like Drea