-- In recent years, advancements in hardware have enabled quadruped robots to operate with high power and speed, while robust locomotion control using reinforcement learning (RL) has also been realized. As a result, expectations are rising for the automation of tasks such as material transport and exploration in unknown environments. However, autonomous locomotion in rough terrains with significant height variations requires vertical movement, and robots capable of performing such movements stably, along with their control methods, have not yet been fully established. In this study, we developed the quadruped robot KLEIYN, which features a waist joint, and aimed to expand quadruped locomotion by enabling chimney climbing through RL. T o facilitate the learning of vertical motion, we introduced Contact-Guided Curriculum Learning (CGCL). As a result, KLEIYN successfully climbed walls ranging from 800 mm to 1000 mm in width at an average speed of 150 mm/s, 50 times faster than conventional robots. Furthermore, we demonstrated that the introduction of a waist joint improves climbing performance, particularly enhancing tracking ability on narrow walls. In recent years, the development of quadruped robots has been actively conducted [1]-[3].

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com One of the great joys in life, Feedback argues, is the perfect opening sentence of a book – and the concomitant realisation that, yes, this one is going to be good. "It was the day my grandmother exploded." "As the manager of the Performance sits before the curtain on the boards and looks into the Fair, a feeling of profound melancholy comes over him in his survey of the bustling place."

Larger language models, such as GPT-3, have shown to be excellent in many tasks. However, we demonstrate that out-of-ordinary questions can throw the model off guard. This work focuses on finding answers to negated complementary questions in commonsense scenarios. We illustrate how such questions adversely affect the model responses. We propose a model-agnostic methodology to improve the performance in negated complementary scenarios. Our method outperforms few-shot generation from GPT-3 (by more than 11 points) and, more importantly, highlights the significance of studying the response of large language models in negated complementary questions. The code, data, and experiments are available under: https://github.com/navidre/negated_complementary_commonsense.

In this post I will go through the steps we took to create a human labelled dataset (i.e. Note that the other approach, and one we will talk about in another post, is to simply let a pre-trained tool do the work of labelling without any human intervention. But we thought that it would be worthwhile to try the human labelling out before seeing what the out-of-the-box results are. I used the photographs in the Claude William Jamson archive, kindly provided by Hull University Archives. This is a collection with a variety of content that lends itself to this kind of experiment.

Deep below the Pacific Northwest is an underwater forest of massive hydrothermal chimneys that stretch for miles across the seafloor. At the Endeavour Segment of the Juan de Fuca Ridge, located 220 miles northwest of Washington State, the seabed is ripping apart and underwater geysers and vents are still forming. Using sonar ships and underwater vehicles, researchers have mapped this area for the first time to reveal 527 chimneys - with some standing nearly 90 feet tall. The spirals are created from a buildup of minerals that flow to the surface in heated liquid -- as hot as 750 degrees Fahrenheit. The hydrothermal chimneys, known as the Endeavour vents, are located in a long, narrow valley that stretches about 8.6 miles long and almost a mile wide.