But the technology could break down trust and social bonds.

Teaching robots to ask for help is key to making them safer and more efficient.

Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know. The technique involves quantifying the fuzziness of human language and using that measurement to tell robots when to ask for further directions. Telling a robot to pick up a bowl from a table with only one bowl is fairly clear. But telling a robot to pick up a bowl when there are five bowls on the table generates a much higher degree of uncertainty — and triggers the robot to ask for clarification.

Because tasks are typically more complex than a simple “pick up a bowl” command, the engineers use large language models (LLMs) — the technology behind tools such as ChatGPT — to gauge uncertainty in complex environments. LLMs are bringing robots powerful capabilities to follow human language, but LLM outputs are still frequently unreliable, said Anirudha Majumdar, an assistant professor of mechanical and aerospace engineering at Princeton and the senior author of a study outlining the new method.

Erwan Plantec, Joachin W.Pedersen, Milton L.Montero, Eleni Nisioti, Sebastian Risi ITU Copenhagen 2024 https://arxiv.org/abs/2406.

OpenRead & Notes Taking.

Following the rapid advancement of artificial intelligence (AI) tools, engineers worldwide have been working on new architectures and hardware components that replicate the organization and functions of the human brain.

Most brain-inspired technologies created to date draw inspiration from the firing of brain cells (i.e., neurons), rather than mirroring the overall structure of neural elements and how they contribute to information processing.

Researchers at Tsinghua University recently introduced a new neuromorphic computational architecture designed to replicate the organization of synapses (i.e., connections between neurons) and the tree-like structure of dendrites (i.e., projections extending from the body of neurons).

A combined team of roboticists from Stanford University and the Toyota Research Institute has found that adding audio data to visual data when training robots helps to improve their learning skills. The team has posted their research on the arXiv preprint server.

The researchers noted that virtually all training done with AI-based robots involves exposing them to a large amount of visual information, while ignoring associated audio. They wondered if adding microphones to robots and allowing them to collect data regarding how something is supposed to sound as it is being done might help them learn a task better.

For example, if a robot is supposed to learn how to open a box of cereal and fill a bowl with it, it may be helpful to hear the sounds of a box being opened and the dryness of the cereal as it cascades down into a bowl. To find out, the team designed and carried out four robot-learning experiments.