4 papers
Knowledge-Guided Manipulation Using Multi-Task Reinforcement Learning
Aditya Narendra, Mukhammadrizo Maribjonov, Dmitry Makarov +2
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…
Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
Nikita Kachaev, Andrei Spiridonov, Andrey Gorodetsky +8
Benchmarks are crucial for evaluating progress in robotics and embodied AI. However, a significant gap exists between benchmarks designed for high-level language instruction follow…
M3PO: Massively Multi-Task Model-Based Policy Optimization
Aditya Narendra, Dmitry Makarov, Aleksandr Panov
We introduce Massively Multi-Task Model-Based Policy Optimization (M3PO), a scalable model-based reinforcement learning (MBRL) framework designed to address sample inefficiency in…
Dynamic Neural Potential Field: Online Trajectory Optimization in the Presence of Moving Obstacles
Aleksei Staroverov, Muhammad Alhaddad, Aditya Narendra +2
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 pr…