The Openclaw agent, which recently went viral for controlling a drone with a natural language prompt, can now autonomously control drones via Mavlink on Dimensional. This open-source development allows the agent to handle perception, tracking, and flight control from a single natural language query.
R2LIVE is a robust, real-time system that integrates LiDAR, inertial, and visual data for state estimation and mapping. This tightly-coupled approach aims to achieve accurate and reliable localization and mapping in various environments.
LeLab is introduced as an innovative platform that enables users to train AI robots without needing to write any code. This tool aims to democratize access to artificial intelligence development and robotics.
Physical AI marks a new era in robotics, integrating advanced AI capabilities into physical systems to interact with the real world. This development promises to revolutionize various industries, from manufacturing to services. It focuses on robots that can learn, adapt, and perform complex tasks autonomously.
This content introduces the challenge of maintaining bio-inspired soft robotics, where traditional sensor methods fail due to the robots' flexible nature. It proposes a novel solution combining probabilistic graph neural networks with zero-trust architecture for effective monitoring and governance.
This research explores combining privacy-preserving machine learning, specifically differential privacy and active learning, for the maintenance of bio-inspired soft robotics. The work addresses the challenge of retraining predictive maintenance models without exposing proprietary data during critical recovery windows.
A personal account details a researcher's struggle with a Decision Transformer failing to maintain bio-inspired soft robotic grippers in real-world deployment, despite high simulation performance. The critical issue identified was the misalignment between the AI's learned policy and human safety expectations for the delicate hardware.
This article discusses the practical challenges of coordinating hundreds of AI agents in robotic swarms, emphasizing that messaging and orchestration issues were more critical than model accuracy. It highlights lessons learned from scaling deployments, such as message storms and stale commands after network flaps, contrasting them with initial naive setups.
The traditional robot maintenance model is ineffective, leading to high costs and downtime. AI-guided service revolutionizes this approach by integrating data to accelerate resolution, increase accuracy, and improve first-visit fix rates.
Jensen Huang showcased a robot autonomously navigating to a night market in Taipei, signifying the emergence of Physical AI from the lab into real-world applications. This event at the intersection of COMPUTEX and GTC declares Physical AI is now an agent navigating cities.
This deep dive analyzes Tesla Inc., highlighting its evolution from an EV manufacturer to a global conglomerate focused on sustainable energy, AI, and robotics. The company's strategic narrative has shifted significantly towards autonomous mobility and AI infrastructure, alongside its core automotive division.
The paper introduces 'Spatial Forcing,' a method for aligning implicit spatial representations in vision-language-action models. It aims to enhance these models' understanding and interaction with spatial information.
goodBytz, a Hamburg-based robotics startup, is supplying fully autonomous robot kitchens to the US Army. Co-founded by Dr. Hendrik Susemihl, the company produces 50,000 meals daily and won the Robotics Award in 2026.
PyRep is presented as a toolkit designed to facilitate deep robot learning by providing an interface between the V-REP robotics simulator and popular deep learning frameworks. It aims to streamline the development and testing of AI-driven robotic applications in a simulated environment.
300 humanoid robots competed in a half-marathon in Beijing, with one robot setting a time faster than the human world record, though the comparison is debated due to differing conditions. The author raises concerns about the integrity of the race, questioning how human referees can effectively monitor 300 robots and 100 teams according to complex rules designed to prevent cheating.
The Google I/O 2026 Dialogues stage provided a recap where leaders discussed the future of artificial intelligence, quantum computing, and robotics. The event also explored the intersection of AI and creativity.
An AI training startup, Shift, is offering free home cleaning in New York in exchange for footage of the work. The goal is to collect video data of domestic labor to train robots for future automation. The service plans to expand to other cities, including London.
NVIDIA introduces Cosmos 3, the first open omni-model for physical AI reasoning and action. This launch marks a significant advancement in merging AI and robotics, enabling AI to interact more intelligently with the physical world.
PRISM is a novel framework that integrates perception (VLM) and decision (LLM) through a dynamic question-answer pipeline, enabling the LLM to actively refine the VLM's output for task-driven scene understanding. This approach significantly outperforms existing image-based models on benchmarks like ALFWorld and Room-to-Room.
This survey addresses the lack of a unified framework for world models, which are internal simulators used in AI for prediction, planning, and reasoning. It proposes a multi-axis taxonomy organizing their diverse aspects like architecture, methodology, reasoning paradigms, and applications across fields such as reinforcement learning and robotics.