Here are three smart tricks, based on an understanding of frictional forces, to beat a slippery slope. I don't know who invented this crazy challenge, but the idea is to put someone in a carved-out ice bowl and see if they can get out. The bowl is shaped like the inside of a sphere, so the higher up the sides you go, the steeper it gets. If you think an icy sidewalk is slippery, try going uphill on an icy sidewalk. What do you do when faced with a problem like this?

Dear Reviewers: Thank you for the comments. We address the main issues and clarify some confusions below. With known external forces and labeled data, they used L-BFGS to optimize the parameters to fit the observed data. They used finite differences to estimate the gradient. For comparison, we run their optimization method in our environments, as requested.

Dear Reviewers: Thank you for the comments. We address the main issues and clarify some confusions below. With known external forces and labeled data, they used L-BFGS to optimize the parameters to fit the observed data. They used finite differences to estimate the gradient. For comparison, we run their optimization method in our environments, as requested.

Large language models (LLMs) are now widely accessible, reaching learners at all educational levels. This development has raised concerns that their use may circumvent essential learning processes and compromise the integrity of established assessment formats. In physics education, where problem solving plays a central role in instruction and assessment, it is therefore essential to understand the physics-specific problem-solving capabilities of LLMs. Such understanding is key to informing responsible and pedagogically sound approaches to integrating LLMs into instruction and assessment. This study therefore compares the problem-solving performance of a general-purpose LLM (GPT-4o, using varying prompting techniques) and a reasoning-optimized model (o1-preview) with that of participants of the German Physics Olympiad, based on a set of well-defined Olympiad problems. In addition to evaluating the correctness of the generated solutions, the study analyzes characteristic strengths and limitations of LLM-generated solutions. The findings of this study indicate that both tested LLMs (GPT-4o and o1-preview) demonstrate advanced problem-solving capabilities on Olympiad-type physics problems, on average outperforming the human participants. Prompting techniques had little effect on GPT-4o's performance, while o1-preview almost consistently outperformed both GPT-4o and the human benchmark. Based on these findings, the study discusses implications for the design of summative and formative assessment in physics education, including how to uphold assessment integrity and support students in critically engaging with LLMs.

Track systems effectively distribute loads, augmenting traction and maneuverability on unstable terrains, leveraging their expansive contact areas. This tracked locomotion capability also aids in hand manipulation of not only regular objects but also irregular objects. In this study, we present the design of a soft robotic finger with an active surface on an omni-adaptive network structure, which can be easily installed on existing grippers and achieve stability and dexterity for in-hand manipulation. The system's active surfaces initially transfer the object from the fingertip segment with less compliance to the middle segment of the finger with superior adaptability. Despite the omni-directional deformation of the finger, in-hand manipulation can still be executed with controlled active surfaces. We characterized the soft finger's stiffness distribution and simplified models to assess the feasibility of repositioning and reorienting a grasped object. A set of experiments on in-hand manipulation was performed with the proposed fingers, demonstrating the dexterity and robustness of the strategy.

Growing robots based on the eversion principle are known for their ability to extend rapidly, from within, along their longitudinal axis, and, in doing so, reach deep into hitherto inaccessible, remote spaces. Despite many advantages, eversion robots also present significant challenges, one of which is maintaining sensory payload at the tip without restricting the eversion process. A variety of tip mechanisms has been proposed by the robotics community, among them rounded caps of relatively complex construction that are not always compatible with functional hardware, such as sensors or navigation pouches, integrated with the main eversion structure. Moreover, many tip designs incorporate rigid materials, reducing the robot's flexibility and consequent ability to navigate through narrow openings. Here, we address these shortcomings and propose a design to overcome them: a soft, entirely fabric based, cylindrical cap that can easily be slipped onto the tip of eversion robots. Having created a series of caps of different sizes and materials, an experimental study was conducted to evaluate our new design in terms of four key aspects: eversion robot made from multiple layers of everting material, solid objects protruding from the eversion robot, squeezability, and navigability. In all scenarios, we can show that our soft, flexible cap is robust in its ability to maintain its position and is capable of transporting payloads such as a camera across long distances.

It is common to utilise dynamic models to measure the tyre-road friction in real-time. Alternatively, predictive approaches estimate the tyre-road friction by identifying the environmental factors affecting it. This work aims to formulate the problem of friction estimation as a visual perceptual learning task. The problem is broken down into detecting surface characteristics by applying semantic segmentation and using the extracted features to predict the frictional force. This work for the first time formulates the friction estimation problem as a regression from the latent space of a semantic segmentation model. The preliminary results indicate that this approach can estimate frictional force.

When Boston Dynamics shares a new robot video, my robophobia levels increase just a little bit. There is something about these robots that get into the uncanny valley for me. This particular video is both fascinating and disturbing. It's fascinating because here are a bunch of robots pulling a truck (not a pickup truck--a real truck). It's disturbing because it shows a BUNCH of robots.

Last week, Stanford researchers revealed that that they had built tiny drones that can open doors. I'm not sure I'm happy about this: How will we keep the robots out of our houses if they can just open the doors? But this is also pretty cool. These tiny drones (or micro air vehicles) are able to pull super heavy loads as compared to their own weight--up to a factor of 40. Well, I guess it's crazy--crazy awesome.

It's been quite some time since I've looked at the physics of a video game. Perhaps it's because I haven't played too many games lately. But whatever the reason, I decided to look at one of my all time favorite mobile games--Bad Piggies. In case you aren't familiar with the game, it's completely different than the Angry Birds games even though it uses the same pigs. The idea in Rovio's Bad Piggies is to build different vehicles to help the pigs get from one point to another. Although the game has its flaws, it's still fun.