The tricky thing about naming the year's biggest losers in tech is that in 2024, it once again felt like everyone lost. Amid the depressing spiral that is social media, the will-they-or-won't-they dance of banning TikTok in the US and the neverending edited and deepfaked content that has everyone questioning what's real, the world lost. But a few areas this year stood out as particularly troubling. Specifically, AI and dedicated AI gadgets proliferated more than ever, spreading not only to our digital assistants and search engines but to our wearables as well. We also saw more deterioration in Intel's standing and bid farewell to a robot maker, as well as Lightning cables.

Startup Embodied is closing down, and its product, an 800 robot for kids ages 5 to 10, will soon be bricked. Embodied blamed its closure on a failed "critical funding round." We had secured a lead investor who was prepared to close the round. However, at the last minute, they withdrew, leaving us with no viable options to continue operations. Despite our best efforts to secure alternative funding, we were unable to find a replacement in time to sustain operations. The company didn't provide further details about the pulled funding.

Children across the US will likely spend the coming days and weeks saying goodbye to an AI-powered friend named Moxie. The small dog-sized companion bot--which used a ChatGPT-style large language model and expressive features to hold open-ended conversations with children--will soon be taken offline due to its creator's financial struggles. The decision to abandon the 799 product four years after its release, first reported by Aftermath, has left some customers bemoaning the loss of an artificial friend and others angrily demanding refunds. Videos of confused, crying children saying goodbye to their companion flooding social media. It's part of a larger trend of companies cutting off software support for hardware to cut costs.

In this work, we study the problem of Embodied Referring Expression Grounding, where an agent needs to navigate in a previously unseen environment and localize a remote object described by a concise high-level natural language instruction. When facing such a situation, a human tends to imagine what the destination may look like and to explore the environment based on prior knowledge of the environmental layout, such as the fact that a bathroom is more likely to be found near a bedroom than a kitchen. We have designed an autonomous agent called Layout-aware Dreamer (LAD), including two novel modules, that is, the Layout Learner and the Goal Dreamer to mimic this cognitive decision process. The Layout Learner learns to infer the room category distribution of neighboring unexplored areas along the path for coarse layout estimation, which effectively introduces layout common sense of room-to-room transitions to our agent. To learn an effective exploration of the environment, the Goal Dreamer imagines the destination beforehand. Our agent achieves new state-of-the-art performance on the public leaderboard of the REVERIE dataset in challenging unseen test environments with improvement in navigation success (SR) by 4.02% and remote grounding success (RGS) by 3.43% compared to the previous state-of-the-art. The code is released at https://github.com/zehao-wang/LAD

Embodied agents are expected to perform more complicated tasks in an interactive environment, with the progress of Embodied AI in recent years. Existing embodied tasks including Embodied Referring Expression (ERE) and other QA-form tasks mainly focuses on interaction in term of linguistic instruction. Therefore, enabling the agent to manipulate objects in the environment for exploration actively has become a challenging problem for the community. To solve this problem, We introduce a new embodied task: Remote Embodied Manipulation Question Answering (REMQA) to combine ERE with manipulation tasks. In the REMQA task, the agent needs to navigate to a remote position and perform manipulation with the target object to answer the question. We build a benchmark dataset for the REMQA task in the AI2-THOR simulator. To this end, a framework with 3D semantic reconstruction and modular network paradigms is proposed. The evaluation of the proposed framework on the REMQA dataset is presented to validate its effectiveness.

With its blue body, big anime eyes, and head shaped like a teardrop, Moxie wants to be friends with your child. The robot companion is designed to support social, emotional and cognitive development in children between the ages of five and 10, through play-based learning and lessons on turn-taking and eye contact. Launched in 2020, Moxie was developed by Embodied, a robotics company based in Pasadena, California. "Our collective vision is to create robots that are going to benefit society," says Paolo Pirjanian, the founder and CEO of Embodied. The roboticist and former NASA scientist founded the company in 2016 and oversaw the creation of Moxie.

Much in the same way that our eyes refresh our view through continual movement, the means to rise up and upend our thinking are found in this tumbling of relations. Unflattening, we remind ourselves of what it is to open our eyes to the world for the first time.¹ There has been a flattening of AI. But first, let's rewind a few seconds… Artificial Intelligence was originally about Strong AI, starting back in the 1950s and even before that under various other names. Strong AI essentially means human-level intelligence. And this was a massive mountain to tackle.

If the death of Jibo taught us anything, it's that it doesn't take much for humans to become emotionally attached to their robot companions. But I suppose that's something we learned when Roomba owners started dressing up their vacuums and kids became obsessed with Tamagotchis. Moxie is a bit different. Developed by Embodied, a company co-founded by former iRobot CTO Paolo Pirjanian, Moxie is a companion robot made specifically for kids to play with every day. During a 15 to 25 minute session, your child can chat with Moxie, play some games, or read alongside it. It can learn to recognize a child's face and their particular learning needs.

Last week, Embodied Inc. launched Moxie, a social robot designed to help children with cognitive development. Moxie uses machine learning and the SocialX platform to perceive and interact. Maja Matarić, interim vice president and vice dean for research at the University of Southern California's Viterbi School of Engineering, co-founded Embodied in 2016. The Pasadena, Calif.-based company said it "has assembled a world-class team of experts in child development, engineering, technology, game design, and entertainment to create Moxie." Embodied has worked with advisors from Disney, MIT, Pixar, and The Jim Henson Co., among others.

Paolo Pirjanian is an Armenia born in Iran and fled to Denmark as a teen. From the time he was young, he was fascinated by computers and started coding in his bedroom. After getting his PhD in robotics, Paolo became an early leader in the field of consumer robotics who has 16 years of experience developing and commercializing cutting-edge home robots. He worked at NASA JPL and led world-class teams and companies at iRobot, Evolution Robotics, and others. In 2016, Paolo founded Embodied, Inc. with the vision to build socially and emotionally intelligent digital companions that improve care and wellness and support people in living better lives every day.