Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates must react to cues of a human teammate's tactical objective to assist in a way that is consistent with the objective (e.g., navigating left or right around an obstacle). To address this challenge, we present Dream2Assist, a framework that combines a rich world model able to infer human objectives and value functions, and an assistive agent that provides appropriate expert assistance to a given human teammate. Our approach builds on a recurrent state space model to explicitly infer human intents, enabling the assistive agent to select actions that align with the human and enabling a fluid teaming interaction. We demonstrate our approach in a high-speed racing domain with a population of synthetic human drivers pursuing mutually exclusive objectives, such as "stay-behind" and "overtake". We show that the combined human-robot team, when blending its actions with those of the human, outperforms the synthetic humans alone as well as several baseline assistance strategies, and that intent-conditioning enables adherence to human preferences during task execution, leading to improved performance while satisfying the human's objective.

De gustibus non est disputandum ("there is no accounting for others' tastes") is a common Latin maxim describing how many solutions in life are determined by people's personal preferences. Many household tasks, in particular, can only be considered fully successful when they account for personal preferences such as the visual aesthetic of the scene. For example, setting a table could be optimized by arranging utensils according to traditional rules of Western table setting decorum, without considering the color, shape, or material of each object, but this may not be a completely satisfying solution for a given person. Toward this end, we present DegustaBot, an algorithm for visual preference learning that solves household multi-object rearrangement tasks according to personal preference. To do this, we use internet-scale pre-trained vision-and-language foundation models (VLMs) with novel zero-shot visual prompting techniques. To evaluate our method, we collect a large dataset of naturalistic personal preferences in a simulated table-setting task, and conduct a user study in order to develop two novel metrics for determining success based on personal preference. This is a challenging problem and we find that 50% of our model's predictions are likely to be found acceptable by at least 20% of people.

Magna is a large Tier 1 automotive supplier that's invented an exciting new light technology for cars: Breakthrough Lighting. Besides its visual appeal and customizability, this technology promises to help self-driving cars communicate with pedestrians. But what is Breakthrough Lighting, and how does it work? Breakthrough Lighting allows lights to seemingly magically appear on a dark surface. Though the Magna Press Release focuses on the rear of the vehicle, this technology could be applied to any physically compatible surface.

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There's booming demand for silicon custom-designed to accelerate AI workloads, as the gobs of cash raised by startups like Hailo Technologies, Graphcore, and Untether AI demonstrates. The fierce competition isn't deterring Blaize (formerly Thinci), which hopes to stand out from the crowd with a novel graph streaming architecture. The nine-year-old startup's claimed system-on-chip performance is impressive, to be fair, which is likely why it's raised nearly $100 million from investors including automotive component maker Denso. Blaize emerged from stealth today with $87 million raised over several venture rounds from strategic and venture backers Denso, Daimler, SPARX Group, Magna, Samsung Catalyst Fund, Temasek, GGV Capital, SGInnovate, and Magna; the second-to-last round closed in September 2018 and totaled $65 million. The company initially focused on what it called vision processors -- chips to speed up vision, radar, and sensor fusion tasks -- before expanding to encompass datacenters, edge infrastructure devices, and enterprise client devices.

Building a self-driving car, it turns out, is a bit like planning a wedding. No matter how much time you give yourself, you risk being overwhelmed by the sheer number of things that need doing. In 2016, when BMW said it would deliver fully self-driving cars, as part of a ride-hailing service, by 2021, it seemed to have plenty of time. But now it has just three years left; in an industry where developing a new car can take seven years, it's a good thing the automaker has gotten around to picking its lidar supplier. Today, BMW struck a deal with industry supplier Magna, and Magna's partner Innoviz, to provide the lidar laser scanners for its self-driving cars.

Magna shares rose more than 4 percent in early trade as executives described the auto supplier's role in a new autonomous venture with Lyft. The Canadian auto-parts supplier said late on Wednesday that it would invest $200 million in Lyft and work with the firm to co-develop self-driving cars.

The companies plan to produce kits that can be installed on existing cars to enable them to operate autonomously, said Raj Kapoor, Lyft's chief strategy officer. Retrofitting cars, as opposed to building new ones, could help the company produce autonomous vehicles more quickly and inexpensively, he said. The announcement Wednesday adds to Lyft's extensive roster of self-driving-vehicle partners, which includes Ford Motor Co.; General Motors Co.; Alphabet's Waymo; nuTonomy Inc.; Tata Motors Ltd.'s Jaguar Land Rover; and Aptiv PLC, formerly Delphi. That stands in contrast to rival Uber Technologies Inc., which is primarily developing self-driving technology on its own rather than forging partnerships. Lyft's goal is to plug autonomous cars from partners into its network. The Magna deal is Lyft's first involving a large-scale manufacturer, and the kits they develop could be sold to auto makers, ride-hailing networks or even drivers themselves, Lyft said.

If Lyft is going to translate self-driving car experiments into production vehicles offering rides, it's going to need some help -- and it's on the way. The company has formed a partnership with Magna that will see the two jointly fund and develop autonomous car systems both for Lyft and the broader automotive industry. Lyft will lead the development, while Magna will take charge of manufacturing as well as contribute its know-how in vehicle systems, driver assistance and safety. The two hope to make the technology available to the industry on a large scale in the "next few years."

Lyft has been criticized for being too nice when it comes to taking advantage of Uber's recent stumbles, but there's a reason for that. Lyft has been growing the number of partnerships with other tech companies and automakers in order to not be left behind in the race to self-driving cars, which would make the company vastly more profitable. SAN FRANCISCO -- Lyft announced Wednesday that it is partnering with Magna, a Canadian automotive parts manufacturer, to develop a self-driving vehicle for its ride-hailing network. As part of the deal, Magna will invest $200 million in Lyft. "We're co-founding and co-developing a self-driving system together," said Lyft co-founder Logan Green.