MIT CSAIL researchers have developed a generative AI system, LucidSim, to train robots in virtual environments for real-world navigation. Using ChatGPT and physics simulators, robots learn to traverse complex terrains. This method outperforms traditional training, suggesting a new direction for robotic training.

A team of roboticists and engineers at MIT CSAIL, Institute for AI and Fundamental Interactions, has developed a generative AI approach to teaching robots how to traverse terrain and move around objects in the real world.

The group has published a paper describing their work and possible uses for it on the arXiv preprint server. They also presented their ideas at the recent Conference on Robot Learning (CORL 2024), held in Munich Nov. 6–9.

Getting robots to navigate in the real world at some point involves teaching them to learn on the fly, or by training them with videos of similar robots in a real-world environment. While such training has proven to be effective in limited environments, it tends to fail when a robot encounters something novel. In this new effort, the team at MIT developed virtual training that better translates to the real world.

Waymo has expanded its robotaxi service to the general public in Los Angeles, allowing anyone with the Waymo One app to request a ride. This marks a significant step in autonomous vehicle technology, as Waymo continues to lead the industry with over 50,000 weekly passengers and a strong safety record.

Waymo on Tuesday opened its robotaxi service to anyone who wants a ride around Los Angeles, marking another milestone in the evolution of self-driving car technology since the company began as a secret project at Google 15 years ago.

The expansion comes eight months after Waymo began offering rides in Los Angeles to a limited group of passengers chosen from a waiting list that had ballooned to more than 300,000 people. Now, anyone with the Waymo One smartphone app will be able to request a ride around an 80-square-mile (129-square-kilometer) territory spanning the second largest U.S. city.

After Waymo received approval from California regulators to charge for rides 15 months ago, the company initially chose to launch its operations in San Francisco before offering a limited service in Los Angeles.

Researchers at Penn Engineering have developed PanoRadar, a system that uses radio waves and AI to provide robots with detailed 3D environmental views, even in challenging conditions like smoke and fog. This innovation offers a cost-effective alternative to LiDAR, enhancing robotic navigation and perception capabilities.

In the race to develop robust perception systems for robots, one persistent challenge has been operating in bad weather and harsh conditions. For example, traditional, light-based vision sensors such as cameras or LiDAR (Light Detection And Ranging) fail in heavy smoke and fog.

However, nature has shown that vision doesn’t have to be constrained by light’s limitations—many organisms have evolved ways to perceive their environment without relying on light. Bats navigate using the echoes of sound waves, while sharks hunt by sensing electrical fields from their prey’s movements.

Radio waves, whose wavelengths are orders of magnitude longer than light waves, can better penetrate smoke and fog, and can even see through certain materials—all capabilities beyond human vision. Yet robots have traditionally relied on a limited toolbox: They either use cameras and LiDAR, which provide detailed images but fail in challenging conditions, or traditional radar, which can see through walls and other occlusions but produces crude, low-resolution images.

Scientists have developed an AI-based authentication scheme to enhance vehicle security in the Internet of Vehicles (IoV).

Scientists claim to have developed an artificial intelligence tool to consolidate the privacy of vehicles and their drivers.

How to preserve the privacy of the so-called Internet of Vehicles (IoV) has emerged as a major challenge due to geographical mobility of vehicles and insufficient resources, the scientists say.

The problem has been aggravated, according to the scientists, due to the “limited resources of onboard units (OBUs)” and the shortcomings of embedded sensors installed in vehicles, which “lure the adversaries to launch various types of attacks.”

Researchers at Tufts University have identified root causes for AI’s poor conversational timing.

When you have a conversation today, notice the natural points when the exchange leaves open the opportunity for the other person to chime in. If their timing is off, they might be taken as overly aggressive, too timid, or just plain awkward.

The back-and-forth is the social element to the exchange of information that occurs in a conversation, and while humans do this naturally—with some exceptions—AI language systems are universally bad at it.

Linguistics and computer science researchers at Tufts have now discovered some of the root causes of this shortfall in AI conversational skills and point to possible ways to make them better conversational partners. Their study results will be presented at the Empirical Methods in Natural Language Processing (EMNLP 2024) conference, held in Miami from November 12 to 16, and are posted to the arXiv preprint server.

TSMC will halt production of AI chips for Chinese firms produced using a 7nm node or lower starting on Monday.

One of the UK’s largest solar farms, a 55 MW project, is now officially online, providing enough power for over 20,000 homes.

The solar farm, developed by Atrato Onsite Energy, is also the fourth largest in the entire country, marking a major milestone for renewable energy in the UK.

The solar farm, which cost £39.4 million to build, is located in Richmond, North Yorkshire, and it covers an impressive 166 acres – that’s about 93 football fields. With over 93,000 bifacial solar panels, this site is expected to reduce CO₂ emissions by 11,000 tonnes annually.

Tesla added more market value Thursday than the combined valuations of top U.S. competitors Ford and General Motors.

A research team led by Professor Bonghoon Kim from DGIST’s Department of Robotics and Mechatronics Engineering has developed a “3D smart energy device” that features both reversible heating and cooling capabilities. Their device was recognized for its excellence and practicality through its selection as the cover article of the international journal Advanced Materials.

The team collaborated with Professor Bongjae Lee from KAIST’s Department of Mechanical Engineering and Professor Heon Lee from Korea University’s Department of Materials Science and Engineering.

Heating and cooling account for approximately 50% of the global energy consumption, contributing significantly to environmental problems such as global warming and air pollution. In response, solar absorption and radiative cooling devices, which harness the sun and outdoor air as heat and cold sources, are gaining attention as eco-friendly and sustainable solutions.

Suspended in the relic of an ancient sea beneath southern Arkansas, there may be enough lithium for nine times the expected global demand for the element in car batteries in 2030.

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A collaborative national and state government research team trained a machine learning model to predict and map the lithium concentrations of salty water deep within the porous limestone aquifer beneath southern Arkansas, known as the Smackover Formation brines.