🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.

robo-gym is an open source toolkit for distributed reinforcement learning on real and simulated robots. robo-gym provides a collection of reinforcement learning environments involving robotic tasks applicable in both simulation and real world robotics.

Feb 9, 2011 · Reinforcement Learning Studio, RL-Studio, is a platform for developing robotic applications with reinforcement learning algorithms. Its modular design allows to work easily with different agents and algoritmhs in autonomous tasks and any simulator.

Feb 5, 2025 · Here we introduce a robotic lifelong reinforcement learning framework that addresses this gap by developing a knowledge space inspired by the Bayesian non-parametric domain. In addition, we...

Implement and compare various learning algorithms to train robot policies for imitation learning, reinforcement learning and model predictive control. Identify sources of distribution shift in robot learning and apply appropriate techniques from online learning to counter it.

In this article, we explore how to optimize task allocation for robot swarms in dynamic environments, emphasizing the necessity of formulating robust, flexible, and scalable strategies for robot cooperation. We introduce a novel framework using a decentralized partially observable Markov decision process (Dec-POMDP), specifically designed for distributed robot swarm networks. At the core of ...

Oct 21, 2024 · Integrating RL with FMs enables these models to achieve desired outcomes and excel at particular tasks. Additionally, RL can be enhanced by leveraging the reasoning and generalization capabilities of FMs. This synergy is revolutionizing various fields, including robotics.

Mar 29, 2025 · Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query formulation. Existing approaches often rely on handcrafted reasoning paths with inductive biases that can limit their overall effectiveness. Motivated by the recent success of reasoning-enhanced models such as DeepSeek ...

4 days ago · Reinforcement Learning for Adaptive Control in Robotics: A Survey. Journal of Intelligent & Robotic Systems, 108(1), 45-66. Deep Reinforcement Learning for Real-world Robotic Applications ...

Mar 27, 2025 · COMPASS integrates vision-based end-to-end imitation learning (IL) with X-Mobility, residual reinforcement learning (RL) in Isaac Lab, and policy distillation methods to scale across different robot platforms. While the IL-based X-Mobility policy is pre-trained on a specific embodiment from data generated using MobilityGen, the generalist ...