For a robot with redundant sensors and actuators distributed throughout its body, it is difficult to construct a controller or a neural network using all of them due to computational cost and complexity. Therefore, it is effective to extract functionally related sensors and actuators, group them, and construct a controller or a network for each of these groups. In this study, the functional and spatial connections among sensors and actuators are embedded into a graph structure and a method for automatic grouping is developed. Taking a musculoskeletal humanoid with a large number of redundant muscles as an example, this method automatically divides all the muscles into regions such as the forearm, upper arm, scapula, neck, etc., which has been done by humans based on a geometric model. The functional relationship among the muscles and the spatial relationship of the neural connections are calculated without a geometric model.

This work studies the challenge of aligning large language models (LLMs) with offline preference data. We focus on alignment by Reinforcement Learning from Human Feedback (RLHF) in particular. While popular preference optimization methods exhibit good empirical performance in practice, they are not theoretically guaranteed to converge to the optimal policy and can provably fail when the data coverage is sparse by classical offline reinforcement learning (RL) results. On the other hand, a recent line of work has focused on theoretically motivated preference optimization methods with provable guarantees, but these are not computationally efficient for large-scale applications like LLM alignment. To bridge this gap, we propose SPAC, a new offline preference optimization method with self-play, inspired by the on-average pessimism technique from the offline RL literature, to be the first provable and scalable approach to LLM alignment. We both provide theoretical analysis for its convergence under single-policy concentrability for the general function approximation setting and demonstrate its competitive empirical performance for LLM alignment on a 7B Mistral model with Open LLM Leaderboard evaluations.

Ouster and Velodyne, two lidar companies, have agreed to a merger in an all-stock transaction, the companies said Monday. Both Ouster and Velodyne will maintain a 50% stake in the new company, according to the agreement that was signed on November 4. The merger comes as many in the industry, including autonomous vehicle technology company Cruise's CEO Kyle Vogt, have been expecting another round of consolidation in the lidar space. That's in part because there are too many lidar companies for how many OEMs are implementing the sensor for autonomous driving applications. It's also because many of these companies, including Ouster and Velodyne, went public via special purpose acquisition (SPAC) at potentially inflated valuations that were based on projected revenue, not actual revenue. Earlier this year, Velodyne acquired AI and lidar company Bluecity.ai,

Argo AI, the self-driving startup backed by Ford and Volkswagen, is shutting down, The Verge has learned. Employees were notified that an announcement would be made late in the day Wednesday. The company, which was founded by veterans of Google and Uber's self-driving car projects, has lost the financial support of Ford and VW, a source said. And according to TechCrunch, the company's resources will be absorbed by both automakers. Argo is estimated to have around 2,000 employees, though it did announce a round of layoffs earlier this year.

Popular gay dating app Grindr has agreed to go public through a blank-cheque firm whose founder was part of a consortium that bought the company in 2020, according to a filing with the US Securities and Exchange Commission on Monday. The deal with Tiga Acquisition Corporation will raise $384m including $284m of the special-purpose acquisition company's (SPAC) cash in trust plus up to $100m in a forward purchase agreement, valuing the company at $2.1bn including debt, according to the filing. The dating app was valued at $620m when it was sold in 2020 by its Chinese owner. Tiga Acquisition Corp went public in November 2020 to raise $240m, a few months after the Grindr sale. The SPAC would have to liquidate later this month if it failed to reach a deal with a potential merger target, after several extensions of the liquidation deadline.

FutureTech II Acquisition Corp. (NASDAQ:FTII) has raised approximately $100 million from an IPO at a price of $10.00 per unit, according to the terms of its most recent S-1/A regulatory filing. The SPAC (Special Purpose Acquisition Company) intends to pursue a merger with a company in the sectors of'disruptive technologies, for example, artificial intelligence, robotics, and any other technology innovations.' My approach is to seek SPACs where the executives have significant industry operating experience as well as at least one SPAC with a track record of success. So, absent those two characteristics, I'm on Hold for FTII at the present time. FutureTech II has 2 executives leading its sponsor, FutureTech II Partners LLC. Chief Executive Officer Yuquan Wang, who was the founding partner of Haiyin Capital and has been a board member of robotics companies and other technology firms.

After forming a partnership with Walmart in July to reoutfit the retailer's distribution network with a fleet of fully autonomous robots, Symbotic has announced plans to become a publicly traded company early next year. Yesterday, the robotics and automation firm announced it will go public via a special acquisition company (SPAC), courtesy of a merger with SoftBank Investment Advisers' SVF Investment Corp 3 (SVFC). Once the merger is finalised in the first half of 2022, the combined company will operate under the name Symbotic and trade on the Nasdaq under the ticker symbol SYM. Both Walmart and Symbotic declined comment following a series of phone calls and emails from Capital.com. In the company release issued on Tuesday, Symbotic chair and CEO Rick Cohen said, "Now is the time to take Symbotic to the next level."

Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods need to iteratively optimize an objective function via a specific deformation model along with meticulous parameter tuning, but which have limited capabilities in registering images with large deformations. While deep learning-based methods can learn the complex mapping from input images to their respective deformation field, it is regression-based and is prone to be stuck at local minima, particularly when large deformations are involved. To this end, we present Stochastic Planner-Actor-Critic (SPAC), a novel reinforcement learning-based framework that performs step-wise registration. The key notion is warping a moving image successively by each time step to finally align to a fixed image. Considering that it is challenging to handle high dimensional continuous action and state spaces in the conventional reinforcement learning (RL) framework, we introduce a new concept `Plan' to the standard Actor-Critic model, which is of low dimension and can facilitate the actor to generate a tractable high dimensional action. The entire framework is based on unsupervised training and operates in an end-to-end manner. We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations. Our empirical results highlight that our work achieves consistent, significant gains and outperforms state-of-the-art methods.

Inceptio Technology, a China-based developer of autonomous trucking technology, raised a $270 million Series B round of funding. Inceptio said the funding will allow it to accelerate the development of its full-stack autonomous driving system called "Xuanyuan" and speed up its deployment in electrification. Inceptio, which was founded in 2018, has raised approximately $490 million to date. It raised a $100 million Series A round in April 2020, followed by an additional $100 million in November 2020. The Series B round was led by JD Logistics, Meituan, and PAG.

The Securities and Exchange Commission (SEC) is poised to put a damper on Special Purpose Acquisition Company (SPAC) IPOs and mergers: it deepened its investigation into potential conflicts of interest in SPAC underwriting processes, and brought charges against prominent SPACs. Find below an analytical digest of the AI SPAC's state of affairs. A special purpose acquisition company (SPAC) is a company with no commercial operations that is formed strictly to raise capital through an IPO for the purpose of acquiring an existing company. IPO investors have no idea what company they ultimately will be investing in.) SPACs seek underwriters and institutional investors before offering shares to the public.