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Why do we love trash and hate slop? I worship at the altar of trash, by which I mean that I am one of those cinephiles with a fondness for bad movies. Perhaps I am underselling the corpus I have in mind by calling it merely "bad," and understating my sect's feeling by calling it merely "fondness." The movies my fellow-cultists and I most esteem are not just bad but incredibly, impressively terrible--scrambled, technically maladroit, incomprehensible at the level of basic plotting--and we are not just fond but obsessed, digging through piles of bric-a-brac at thrift stores in hope of uncovering forgotten VHS tapes from the eighties, scrolling to neglected corners of YouTube with the goal of alighting on some near-unwatchable monstrosity with thirty-two views and a handful of affronted comments. But we, too, are discerning; we do not approve of just any bad movie. The train wrecks we love feature amateur actors and dialogue so garbled that it may well have been translated from a foreign language by a non-native speaker (as it probably was in the case of "Troll 2," about which more later). The sound quality is scratchy; the camerawork is nauseating, and not in a chicly experimental way. The movies in the good-because-bad canon are not forgettably bad, like cheesy Hallmark fare or the thousandth predictable sequel in a superhero franchise; they are not skillfully and expensively bad, like botched Oscar bait. Nor are they quite like the gripping yet ultimately shallow entertainments that Pauline Kael defended in her classic 1969 essay "Trash, Art, and the Movies," published in . No, our bad movies are monumental feats of incompetence, inspired in their badness, bad in ways I could never have dreamed a film could be bad, so feverishly and outlandishly bad that they have bodied forth a new aesthetic category. These trash movies, as I have taken to calling them, to distinguish them from prestige failures, do not quite constitute a genre, the way the thriller or the rom-com does. They do not treat any subjects in particular, or hew to any specific conventions, or share any consistent visual signature.

How do garbage trucks work? Behind every trash day is a powerful, sophisticated machine. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Peek inside the powerful machines that collect, compact, and haul away your trash. Breakthroughs, discoveries, and DIY tips sent six days a week.

Make full use of the 15GB you get in Gmail, Photos, and Drive for free. Breakthroughs, discoveries, and DIY tips sent six days a week. If Google keeps bothering you to pay for cloud storage, it's not just you. You only get a relatively measly 15GB of storage free of charge with a Google account, and you have to split that across Gmail, Google Photos, and Google Drive. That 15GB can fill up quickly, but it doesn't have to.

Michio Okada first conceptualised "weak robots", which have limited capabilities themselves, and are framed as objects or "social others" which people are invited to assist and take care of. In Okada's work, such robots are used to invite pro-social behaviour from people, such as encouraging them to pick up trash to assist a trash can robot (Okada (2022)). We conceptualise human-robot interaction (HRI) as a stage where weak robots-- designed to be "cute" and vulnerable--play the role of incidental actors that subvert the person engaging with them. Caudwell and Lacey (2020) argue that cuteness as a design choice for robots can encourage users to trust and form relationships with those robots, which introduces ambivalent power dynamics through the production of intimacy . In fact, cuteness can also be seen as a deceptive or "dark" pattern, due to the utilisation of cuteness to prompt affective responses which can be used to collect emotional data, as well as some degree of reduction of user agency (Lacey and Caudwell (2019)). The ability and affordances of cute and weak robots to influence user behaviour merits the discussion of their ethicality, which we do in this paper through design fiction. Unlike traditional HRI research, often confined to laboratory settings, our focus is on spontaneous, real-world interactions that transform everyday environments into sites of performative potential. We argue that the theatricality of these encounters is central to understanding their impact: the presence of a weak and/or cute robot, such as the trash can robot, developed by Okada and the Interaction and Communication Design Lab of the T oyohashi University of T echnology, acts as a disruptive interloper that introduces an observer's effect and, thus, affects the human interlocutors. First, we examine the concept of weak robots through the lens of performativity theory as well as concepts of machine (dys)function.

This work investigates how semantics influence localisation performance and robustness in a learned self-supervised, contrastive semantic localisation framework. After training a localisation network on both original and perturbed maps, we conduct a thorough post-hoc introspection analysis to probe whether the model filters environmental noise and prioritises distinctive landmarks over routine clutter. We validate various interpretability methods and present a comparative reliability analysis. Integrated gradients and Attention Weights consistently emerge as the most reliable probes of learned behaviour. A semantic class ablation further reveals an implicit weighting in which frequent objects are often down-weighted. Overall, the results indicate that the model learns noise-robust, semantically salient relations about place definition, thereby enabling explainable registration under challenging visual and structural variations.

The word dimension for the embedding layer is 300. In Tab. 7 we further evaluate the diversity of COS-CVAE using self-CIDEr We provide additional qualitative results in Tabs. In Tab. 12 we show the divserse captions for novel objects generated by our model and the regions The evaluation server for nocaps accepts only one caption per image and does not support methods modeling one-to-many relationships for images and captions. In Figure 1 (left) we show the average accuracy and diversity scores again averaged across annotators; in Figure 1 (right) we show the accuracy and diversity scores from each annotator. We find that the captions generated by the COS-CV AE are scored to be more accurate compared to COS-CV AE (paired).

Breakthroughs, discoveries, and DIY tips sent every weekday. For some adventurers, scaling Mount Everest represents the ultimate test of grit and determination: a visual signifier of humanity's epic struggle to overcome the elements. For others, the peak can seem more like a really tall trash can. Every year, around 600 climbers make the trek from the mountain's base camp to the summit. During their time on Everest, each person produces an estimated 18 pounds of waste, most of which is left behind.

End-to-end visuomotor policies trained using behavior cloning have shown a remarkable ability to generate complex, multi-modal low-level robot behaviors. However, at deployment time, these policies still struggle to act reliably when faced with out-of-distribution (OOD) visuals induced by objects, backgrounds, or environment changes. Prior works in interactive imitation learning solicit corrective expert demonstrations under the OOD conditions -- but this can be costly and inefficient. We observe that task success under OOD conditions does not always warrant novel robot behaviors. In-distribution (ID) behaviors can directly be transferred to OOD conditions that share functional similarities with ID conditions. For example, behaviors trained to interact with in-distribution (ID) pens can apply to interacting with a visually-OOD pencil. The key challenge lies in disambiguating which ID observations functionally correspond to the OOD observation for the task at hand. We propose that an expert can provide this OOD-to-ID functional correspondence. Thus, instead of collecting new demonstrations and re-training at every OOD encounter, our method: (1) detects the need for feedback by first checking if current observations are OOD and then identifying whether the most similar training observations show divergent behaviors, (2) solicits functional correspondence feedback to disambiguate between those behaviors, and (3) intervenes on the OOD observations with the functionally corresponding ID observations to perform deployment-time generalization. We validate our method across diverse real-world robotic manipulation tasks with a Franka Panda robotic manipulator. Our results show that test-time functional correspondences can improve the generalization of a vision-based diffusion policy to OOD objects and environment conditions with low feedback.