Unfortunately PBS is often computationally expensive and rarely differentiable, which limits the scope of downstream applications.

In recent years, many countries, including Japan, have rapidly aging populations, making the preservation of seniors' quality of life a significant concern. For elderly people with impaired physical abilities, support for toileting is one of the most important issues. This paper details the design, development, experimental assessment, and potential application of the gripper system, with a focus on the unique requirements and obstacles involved in aiding elderly or hemiplegic individuals in dressing and undressing trousers. The gripper we propose seeks to find the right balance between compliance and grasping forces, ensuring precise manipulation while maintaining a safe and compliant interaction with the users. The gripper's integration into a custom--built robotic manipulator system provides a comprehensive solution for assisting hemiplegic individuals in their dressing and undressing tasks. Experimental evaluations and comparisons with existing studies demonstrate the gripper's ability to successfully assist in both dressing and dressing of trousers in confined spaces with a high success rate. This research contributes to the advancement of assistive robotics, empowering elderly, and physically impaired individuals to maintain their independence and improve their quality of life.

Garment manipulation is a critical challenge due to the diversity in garment categories, geometries, and deformations. Despite this, humans can effortlessly handle garments, thanks to the dexterity of our hands. However, existing research in the field has struggled to replicate this level of dexterity, primarily hindered by the lack of realistic simulations of dexterous garment manipulation. Therefore, we propose DexGarmentLab, the first environment specifically designed for dexterous (especially bimanual) garment manipulation, which features large-scale high-quality 3D assets for 15 task scenarios, and refines simulation techniques tailored for garment modeling to reduce the sim-to-real gap. Previous data collection typically relies on teleoperation or training expert reinforcement learning (RL) policies, which are labor-intensive and inefficient. In this paper, we leverage garment structural correspondence to automatically generate a dataset with diverse trajectories using only a single expert demonstration, significantly reducing manual intervention. However, even extensive demonstrations cannot cover the infinite states of garments, which necessitates the exploration of new algorithms. To improve generalization across diverse garment shapes and deformations, we propose a Hierarchical gArment-manipuLation pOlicy (HALO). It first identifies transferable affordance points to accurately locate the manipulation area, then generates generalizable trajectories to complete the task. Through extensive experiments and detailed analysis of our method and baseline, we demonstrate that HALO consistently outperforms existing methods, successfully generalizing to previously unseen instances even with significant variations in shape and deformation where others fail. Our project page is available at: https://wayrise.github.io/DexGarmentLab/.

Unfortunately PBS is often computationally expensive and rarely differentiable, which limits the scope of downstream applications.

Cloth folding is a complex task due to the inevitable self-occlusions of clothes, their complicated dynamics, and the disparate materials, geometries, and textures that garments can have. In this work, we learn folding actions conditioned on text commands. Translating high-level, abstract instructions into precise robotic actions requires sophisticated language understanding and manipulation capabilities. To do that, we leverage a pre-trained vision-language model and repurpose it to predict manipulation actions. Our model, BiFold, can take context into account and achieves state-of-the-art performance on an existing language-conditioned folding benchmark. Given the lack of annotated bimanual folding data, we devise a procedure to automatically parse actions of a simulated dataset and tag them with aligned text instructions. BiFold attains the best performance on our dataset and can transfer to new instructions, garments, and environments.

From the steam engine in 1712 to the first ever iPhone in 2007, each year sees the birth of ever more incredible inventions. And after a year of mind-boggling tech, it's clear that 2024 has been no exception to the rule. The last 12 months have seen brilliant minds from around the world creating some mind-blowing and potentially world-changing breakthroughs. With 2024 almost at its end, MailOnline has taken a look back at some of this year's coolest gadgets and most exciting innovations. From an AI for designing proteins to a real-life pair of Wallace and Gromit's'techno trousers', these inventions are a glimpse of how we all might be living in the future. And when it comes to big breakthroughs, this year has been a resounding success for billionaire Elon Musk.

Referring Multi-Object Tracking (RMOT) is an important topic in the current tracking field. Its task form is to guide the tracker to track objects that match the language description. Current research mainly focuses on referring multi-object tracking under single-view, which refers to a view sequence or multiple unrelated view sequences. However, in the single-view, some appearances of objects are easily invisible, resulting in incorrect matching of objects with the language description. In this work, we propose a new task, called Cross-view Referring Multi-Object Tracking (CRMOT). It introduces the cross-view to obtain the appearances of objects from multiple views, avoiding the problem of the invisible appearances of objects in RMOT task. CRMOT is a more challenging task of accurately tracking the objects that match the language description and maintaining the identity consistency of objects in each cross-view. To advance CRMOT task, we construct a cross-view referring multi-object tracking benchmark based on CAMPUS and DIVOTrack datasets, named CRTrack. Specifically, it provides 13 different scenes and 221 language descriptions. Furthermore, we propose an end-to-end cross-view referring multi-object tracking method, named CRTracker. Extensive experiments on the CRTrack benchmark verify the effectiveness of our method. The dataset and code are available at https://github.com/chen-si-jia/CRMOT.

Multi-task learning of deformable object manipulation is a challenging problem in robot manipulation. Most previous works address this problem in a goal-conditioned way and adapt goal images to specify different tasks, which limits the multi-task learning performance and can not generalize to new tasks. Thus, we adapt language instruction to specify deformable object manipulation tasks and propose a learning framework. We first design a unified Transformer-based architecture to understand multi-modal data and output picking and placing action. Besides, we have introduced the visible connectivity graph to tackle nonlinear dynamics and complex configuration of the deformable object. Both simulated and real experiments have demonstrated that the proposed method is effective and can generalize to unseen instructions and tasks. Compared with the state-of-the-art method, our method achieves higher success rates (87.2% on average) and has a 75.6% shorter inference time. We also demonstrate that our method performs well in real-world experiments.

It's been 30 years since beloved British duo Wallace and Gromit embarked on probably their best loved adventure in'The Wrong Trousers'. In the classic 1993 stop motion film by Nick Park, Gromit receives a pair of'ex-NASA' robotic techno trousers from Wallace for his birthday. They prove extremely useful when Gromit redecorates his bedroom, but lead to trouble when they fall into the clutches of the villainous penguin, Feathers McGraw. Although for now consigned to the fictional world of the loveable duo, experts think they could be built – and conceivably let a wearer walk on walls and even ceilings. Dr Katie Raymer, a physicist and PhD graduate from the University of Leicester, said a real-life pair would use powerful vacuum suction at the soles, just like in the film. In the classic film, Gromit receives a pair of ex-NASA robotic'techno trousers' from Wallace for his birthday, which allows the wearer to walk on walls and even ceilings.

In this paper we and Cognitive Science, there is a pervasive idea that argue that explanations, while not always perfectly accurate humans employ mental representations in order to in regards to reality (due to the existence of hidden factors), navigate the world and make predictions about outcomes are best structured and involve the same conceptual basis as of future actions. By understanding how decisions, and that when trying to understand an explanation these representational structures work, we not only we do so by simulating the decision-making process through understand more about human cognition but also the explanation provided. In other words, what makes a good gain a better understanding for how humans rationalise explanation of an agent-based action is that it presents us with and explain decisions. This has an influencing a reasoning structure that we can follow and relate to our own effect on explainable AI, where the goal is to decision-making processes. It is thus imperative that the explanations provide explanations of computer decision-making provided by artificial agents, in the context of XAI, for a human audience. We show that the Contextual not only provide deliberations that we can follow, but more Importance and Utility method for XAI share importantly provide them in a conceptual framework which an overlap with the current new wave of actionoriented facilitates retreading the deliberation and is context-sensitive.