Abstract Nowadays, Multi Robotic System (MRS) consisting of different robot shapes, sizes
and capabilities has received significant attention from researchers and are being deployed …

Imitation learning from human demonstrations is a promising paradigm for teaching robots
manipulation skills in the real world. However, learning complex long-horizon tasks often …

Human can leverage prior experience and learn novel tasks from a handful of
demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve …

In this work, we introduce SMART-LLM, an innovative framework designed for embodied
multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task Planning using Large …

Human demonstration videos are a widely available data source for robot learning and an
intuitive user interface for expressing desired behavior. However, directly extracting …

This article reviews meta-learning also known as learning-to-learn which seeks rapid and
accurate model adaptation to unseen tasks with applications in highly automated AI, few …

We present a framework for learning hierarchical policies from demonstrations, using sparse
natural language annotations to guide the discovery of reusable skills for autonomous …

Simulation is the key to scaling up validation and verification for robotic systems such as
autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a …

Abstract We introduce Compositional Imitation Learning and Execution (CompILE): a
framework for learning reusable, variable-length segments of hierarchically-structured …

Model-based reinforcement learning (RL) is a sample-efficient way of learning complex
behaviors by leveraging a learned single-step dynamics model to plan actions in …