The contextual duelling bandit problem models adaptive recommender systems, where the algorithm presents a set of items to the user, and the user's choice reveals their preference. This setup is well suited for implicit choices users make when navigating a content platform, but does not capture other possible comparison queries. Motivated by the fact that users provide more reliable feedback after consuming items, we propose a new bandit model that can be described as follows. The algorithm recommends one item per time step; after consuming that item, the user is asked to compare it with another item chosen from the user's consumption history. Importantly, in our model, this comparison item can be chosen without incurring any additional regret, potentially leading to better performance. However, the regret analysis is challenging because of the temporal dependency in the user's history. To overcome this challenge, we first show that the algorithm can construct informative queries provided the history is rich, i.e., satisfies a certain diversity condition. We then show that a short initial random exploration phase is sufficient for the algorithm to accumulate a rich history with high probability. This result, proven via matrix concentration bounds, yields $O(\sqrt{T})$ regret guarantees. Additionally, our simulations show that reusing past items for comparisons can lead to significantly lower regret than only comparing between simultaneously recommended items.

Autonomous exploration of unknown environments using a team of mobile robots demands distributed perception and planning strategies to enable efficient and scalable performance. Ideally, each robot should update its map and plan its motion not only relying on its own observations, but also considering the observations of its peers. Centralized solutions to multi-robot coordination are susceptible to central node failure and require a sophisticated communication infrastructure for reliable operation. Current decentralized active mapping methods consider simplistic robot models with linear-Gaussian observations and Euclidean robot states. In this work, we present a distributed multi-robot mapping and planning method, called Riemannian Optimization for Active Mapping (ROAM). We formulate an optimization problem over a graph with node variables belonging to a Riemannian manifold and a consensus constraint requiring feasible solutions to agree on the node variables. We develop a distributed Riemannian optimization algorithm that relies only on one-hop communication to solve the problem with consensus and optimality guarantees. We show that multi-robot active mapping can be achieved via two applications of our distributed Riemannian optimization over different manifolds: distributed estimation of a 3-D semantic map and distributed planning of SE(3) trajectories that minimize map uncertainty. We demonstrate the performance of ROAM in simulation and real-world experiments using a team of robots with RGB-D cameras.

Sonos makes some of our favorite speakers and soundbars-- we've tested all of them and have yet to find one we dislike. These devices are expensive, but the good news is that Black Friday Sonos deals are here (even if it's not Black Friday yet). We've included the best deals below. And make sure to read our Best Early Black Friday Deals roundup for more discounts. WIRED tests products year-round and handpicked these deals based on the actual discounts, not just the discounts retailers claim to offer.

To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios during deployment, by drawing upon a diverse repertoire of previously learned behaviors. Our approach, RObust Autonomous Modulation (ROAM), introduces a mechanism based on the perceived value of pre-trained behaviors to select and adapt pre-trained behaviors to the situation at hand. Crucially, this adaptation process all happens within a single episode at test time, without any human supervision. We provide theoretical analysis of our selection mechanism and demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped, even successfully moving forward with roller skates on its feet. Our approach adapts over 2x as efficiently compared to existing methods when facing a variety of out-of-distribution situations during deployment by effectively choosing and adapting relevant behaviors on-the-fly.

As deep learning models continue to increase in size, the memory requirements for training have surged. While high-level techniques like offloading, recomputation, and compression can alleviate memory pressure, they also introduce overheads. However, a memory-efficient execution plan that includes a reasonable operator execution order and tensor memory layout can significantly increase the models' memory efficiency and reduce overheads from high-level techniques. In this paper, we propose ROAM which operates on computation graph level to derive memory-efficient execution plan with optimized operator order and tensor memory layout for models. We first propose sophisticated theories that carefully consider model structure and training memory load to support optimization for large complex graphs that have not been well supported in the past. An efficient tree-based algorithm is further proposed to search task divisions automatically, along with delivering high performance and effectiveness to solve the problem. Experiments show that ROAM achieves a substantial memory reduction of 35.7%, 13.3%, and 27.2% compared to Pytorch and two state-of-the-art methods and offers a remarkable 53.7x speedup. The evaluation conducted on the expansive GPT2-XL further validates ROAM's scalability.

The widely used chatbot ChatGPT was designed to generate digital text, everything from poetry to term papers to computer programs. But when a team of artificial intelligence researchers at the computer chip company Nvidia got their hands on the chatbot's underlying technology, they realized it could do a lot more. Within weeks, they taught it to play Minecraft, one of the world's most popular video games. Inside Minecraft's digital universe, it learned to swim, gather plants, hunt pigs, mine gold and build houses. "It can go into the Minecraft world and explore by itself and collect materials by itself and get better and better at all kinds of skills," said a Nvidia senior research scientist, Linxi Fan, who is known as Jim.

Controlling complex tasks in robotic systems, such as circular motion for cleaning or following curvy lines, can be dealt with using nonlinear vector fields. In this paper, we introduce a novel approach called rotational obstacle avoidance method (ROAM) for adapting the initial dynamics when the workspace is partially occluded by obstacles. ROAM presents a closed-form solution that effectively avoids star-shaped obstacles in spaces of arbitrary dimensions by rotating the initial dynamics towards the tangent space. The algorithm enables navigation within obstacle hulls and can be customized to actively move away from surfaces, while guaranteeing the presence of only a single saddle point on the boundary of each obstacle. We introduce a sequence of mappings to extend the approach for general nonlinear dynamics. Moreover, ROAM extends its capabilities to handle multi-obstacle environments and provides the ability to constrain dynamics within a safe tube. By utilizing weighted vector-tree summation, we successfully navigate around general concave obstacles represented as a tree-of-stars. Through experimental evaluation, ROAM demonstrates superior performance in terms of minimizing occurrences of local minima and maintaining similarity to the initial dynamics, outperforming existing approaches in multi-obstacle simulations. The proposed method is highly reactive, owing to its simplicity, and can be applied effectively in dynamic environments. This was demonstrated during the collision-free navigation of a 7 degree-of-freedom robot arm around dynamic obstacles

Your first apartment after graduation is probably not your forever home, but you can make it something you're proud of with gadgets that do your bidding. You can automate your lights, keep an eye on your pets and clean up your floors more efficiently with relatively affordable devices that won't eat up too much of your paycheck. We've tried out a lot of smart home tech over the years and here's what we recommend for newbies and those with tight budgets. Think of the smart display as your smart home command center. This one works with Alexa, fits just about anywhere and is comparatively inexpensive.

While plenty of gadgets cross our desks, we at Engadget also end up buying a lot of things for ourselves throughout the year. In 2021, some of us invested in smart home devices and others (re)discovered passions for things like e-books and vinyl, but there are plenty of things we bought and loved that didn't make it onto the site. Here, our staffers look back on the year that was by gushing about their favorite items they bought this year. After a few years of waffling, I finally pulled the trigger in 2021 and bought a Dyson stick vacuum. You could say I fell for the hype, but honestly it's been one of my favorite purchases of the year and arguably the most useful. Until now, we had been relying on a few-years-old Roomba (lovingly named Dale) to clean our two-bedroom apartment -- Dale did a good job, but the Dyson is even better.

Summer is here and you're probably ready to party, see your friends, and mostly get back to yucking it up responsibly outdoors. But as plenty of people aren't comfortable venturing into the crowded world just yet, you're still going to spend time indoors. Either way, you'll want some tunes, and Sonos has made a speaker pretty well-suited for both environments. While the Sonos Roam has a few shortcomings, it's pretty impressive. But when traditional Bluetooth speakers have gotten pretty inexpensive, is it worth the hefty $169 price tag?