Learning in simulation and applying the learned policy to the real world is a potential approach to enable generalist robots, and solve complex decision-making tasks. However, the challenge to this approach is to address simulation-to-reality (sim-to-real) gaps. Also, a huge amount of data is needed while learning to solve these tasks, and the load of…

The practical application of robotic technology in automatic assembly processes holds immense value. However, traditional robotic systems have struggled to adapt to the demands of production environments characterized by high-mix, low-volume manufacturing. Robotic learning presents a potential solution to this challenge by enabling robots to acquire assembly skills through demonstration rather than scripted trajectories, thus…

The exploration of artificial intelligence within dynamic 3D environments has emerged as a critical area of research, aiming to bridge the gap between static AI applications and their real-world usability. Researchers at Google DeepMind have pioneered this realm, developing sophisticated agents capable of interpreting and acting on complex instructions within various simulated settings. This new…

Reinforcement learning has exhibited notable empirical success in approximating solutions to the Hamilton-Jacobi-Bellman (HJB) equation, consequently generating highly dynamic controllers. However, the inability to bind the suboptimality of resulting controllers or the approximation quality of the true cost-to-go function due to finite sampling and function approximators has limited the broader application of such methods.  Consequently,…

The problem of sparsity and degeneracy issues in LiDAR SLAM has been addressed by introducing Quatro++, a robust global registration framework developed by researchers from the KAIST. This method has surpassed previous success rates and improved loop closing accuracy and efficiency through ground segmentation. Quatro++ exhibits significantly superior loop closing performance, resulting in higher quality…

When a whirring sound catches your attention, you’re walking down the bustling city street, carefully cradling your morning coffee. Suddenly, a knee-high delivery robot zips past you on the crowded sidewalk. With remarkable dexterity, it smoothly avoids colliding into pedestrians, strollers, and obstructions, deftly plotting a clear path forward. This isn’t some sci-fi scene –…

NVIDIA’s unveiling of Project GR00T, a unique foundation model for humanoid robots, and its commitment to the Isaac Robotics Platform and the Robot Operating System (ROS) heralds a significant leap in the development and application of AI in robotics. This project promises to revolutionize how robots understand and interact with the world around them, equipping…

In robotics, natural language is an accessible interface for guiding robots, potentially empowering individuals with limited training to direct behaviors, express preferences, and offer feedback. Recent studies have underscored the inherent capabilities of large language models (LLMs), pre-trained on extensive internet data, in addressing various robotics tasks. These tasks range from devising action sequences based…

Numerous challenges underlying human-robot interaction exist. One such challenge is enabling robots to display human-like expressive behaviors. Traditional rule-based methods need more scalability in new social contexts, while the need for extensive, specific datasets limits data-driven approaches. This limitation becomes pronounced as the variety of social interactions a robot might encounter increases, creating a demand…

Since it enables humans to teach robots any skill, imitation learning via human-provided demonstrations is a promising approach for creating generalist robots. Lane-following in mobile robots, basic pick-and-place manipulation, and more delicate manipulations like spreading pizza sauce or inserting a battery may all be taught to robots through direct behavior cloning. However, rather than merely…