4 accepted papers
Generalist robot policies must operate safely and reliably in everyday human environments such as homes, offices, and warehouses, where people and objects move unpredictably. We present Dynamic Neural Potential Field (NPField-GPT), a learning-enhanced model predictive control (MPC) framework that co…
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…
Conformal Prediction (CP) is an uncertainty quantification framework that provides prediction sets with a user-specified probability to include the true class in the prediction set. This guarantee on the user-specified probability is known as marginal coverage. Marginal coverage refers to the probab…
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