Rivian will provide 50,000 robotaxis to Uber in a deal worth $1.25 billion Initial deployments will start in San Francisco and Miami. Rivian and Uber, with the former to provide the latter with 50,000 robotaxis in funding. This starts with Uber purchasing 10,000 Rivian R2 robotaxis, which will be deployed in San Francisco and Miami by 2028. If all goes well, Uber will scoop up 40,000 more robotaxis by 2030. The company plans to scale the initiative to 25 major cities by 2031.

Uber is expanding its Women Preferences feature nationwide, aiming for increased safety and comfort during rides across the United States.

Uber plans to deploy 20,000 robotaxis across U.S. and international markets over six years, with rides available exclusively through the Uber app.

Where Are All the New Cars? New cars were scant at CES this year, largely because the center of gravity for the auto world has moved--technologically and geographically--to China. This robotaxi built by Uber, Lucid, and Nuro was one of the few cars announced at CES, and it's not even one you can buy. Some years ago now, a very senior Mercedes executive in the US confided in me that CES was "the second-most important car show in the world, after Detroit." Before the auto world's full-on EV boom, this was quite the thing to admit--shocking, in fact--but it marked the subsequent carmaker takeover of the world's largest tech show. This year in Las Vegas, however, the cars were almost nowhere to be seen.

Reward-free data is abundant and contains rich prior knowledge of human behaviors, but it is not well exploited by offline reinforcement learning (RL) algorithms. In this paper, we propose UBER, an unsupervised approach to extract useful behaviors from offline reward-free datasets via diversified rewards. UBER assigns different pseudo-rewards sampled from a given prior distribution to different agents to extract a diverse set of behaviors, and reuse them as candidate policies to facilitate the learning of new tasks. Perhaps surprisingly, we show that rewards generated from random neural networks are sufficient to extract diverse and useful behaviors, some even close to expert ones. We provide both empirical and theoretical evidences to justify the use of random priors for the reward function. Experiments on multiple benchmarks showcase UBER's ability to learn effective and diverse behavior sets that enhance sample efficiency for online RL, outperforming existing baselines. By reducing reliance on human supervision, UBER broadens the applicability of RL to real-world scenarios with abundant reward-free data.

When you purchase through links in our articles, we may earn a small commission. Hundreds of websites and online services are currently unavailable due to a major Cloudflare service issue. Another day, another worldwide outage of a critical online service that brings numerous websites (including several high-profile ones) down to their knees. This news comes hot on the heels of last month's Amazon cloud outage, which took down everything from web services to Ring video doorbells to smart beds that cooked their owners . Cloudflare is a US-based company that operates hundreds of data centers, serves as a content delivery network for millions of websites, provides security and DDoS mitigation services for many of those websites, and a lot more.

Reward-free data is abundant and contains rich prior knowledge of human behaviors, but it is not well exploited by offline reinforcement learning (RL) algorithms. In this paper, we propose UBER, an unsupervised approach to extract useful behaviors from offline reward-free datasets via diversified rewards.

There were still a ways off, he acknowledged--five or 10 or 15 years. But to him, Uber's role was clear. "Are we going to be part of the future?" he said. "Or are we going to resist the future, like that taxi industry before us? For us, we're a tech company, so we've said, let's be part of that."

Uber will invest 300m in electric vehicle maker Lucid in a robotaxi deal that aims to start with one major US city late next year. The two companies announced the new partnership on Thursday. Over six years starting in 2026, Uber will acquire and deploy over 20,000 Lucid Gravity SUVs that will be equipped with autonomous vehicle (AV) technology from startup Nuro, the three companies said in a statement. The agreement illustrates the renewed plans and push for financing for self-driving cabs, years after a first wave of autonomous driving investment produced only a limited number of vehicles. Tesla has recently launched a robotaxi trial in Austin, and Alphabet's driverless taxi unit, Waymo, is speeding up its expansion.