Foundation models and large language models have shown immense human-like understanding and capabilities for generating text and digital media. However, foundation models that can freely sense, interact, and actuate the physical world like in the digital domain is far from being realized. This is due to a number of challenges including: 1) being constrained to the types of static devices and sensors deployed, 2) events often being localized to one part of a large space, and 3) requiring dense and deployments of devices Figure 1: RASP autonomous payload reconfiguration to achieve full coverage. As a critical step towards enabling to execute user specified task foundation models to successfully and freely interact with the physical environment, we propose RASP, a modular and Scaling up, there are few works that explore the use reconfigurable sensing and actuation platform that allows of LLMs to actuate our environments, particularly smart drones to autonomously swap onboard sensors and actuators homes [16, 17, 32], where events and actions may occur anywhere in only 25 seconds, allowing a single drone to quickly adapt in the space. These works generally focus on adapting to a diverse range of tasks. We demonstrate through real LLMs as a human-like interface to actuate common internetconnected smart home deployments that RASP enables FMs and LLMs smart appliances (e.g., speakers, television, air to complete diverse tasks up to 85% more successfully by conditioning, etc.). Much like how FMs enable general human allowing them to target specific areas with specific sensors language and sensory understanding and responses, and actuators on-the-fly.

Today, muscle atrophy is often unavoidable when you can't move due to severe injury, old age or diseases like amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS). However, Harvard researchers see hope in soft robotics that could someday stretch and contract the muscles of patients unable to do so themselves. The Harvard engineers tested a new mechanostimulation system on mice, successfully preventing or assisting in their recovery from muscle atrophy. The team implanted the "soft robotic device" on a mouse's hind limb, which they immobilized in a cast-like enclosure for around two weeks. While the control group's untreated muscles wasted away as expected, the actively stimulated muscles showed reduced degradation.

Exploration in novel settings can be challenging without prior experience in similar domains. However, humans are able to build on prior experience quickly and efficiently. Children exhibit this behavior when playing with toys. For example, given a toy with a yellow and blue door, a child will explore with no clear objective, but once they have discovered how to open the yellow door, they will most likely be able to open the blue door much faster. Adults also exhibit this behavior when entering new spaces such as kitchens. We develop a method, Contextual Prior Prediction, which provides a means of transferring knowledge between interactions in similar domains through vision. We develop agents that exhibit exploratory behavior with increasing efficiency, by learning visual features that are shared across environments, and how they correlate to actions. Our problem is formulated as a Contextual Multi-Armed Bandit where the contexts are images, and the robot has access to a parameterized action space. Given a novel object, the objective is to maximize reward with few interactions. A domain which strongly exhibits correlations between visual features and motion is kinemetically constrained mechanisms. We evaluate our method on simulated prismatic and revolute joints.

Aegis AI, an artificial intelligence startup that builds software which employs computer vision to automatically detect weapons in security camera feeds, today announced that it's rebranding as Actuate and launched new AI threat-detection features. Actuate was founded in early 2018 by University of Chicago MBAs Sonny Tai and Ben Ziomek. Tai is a former Marine Corps captain who spent his formative years in Johannesburg, South Africa, where gun violence rates are some of the highest in the world, while Ziomek brings deep data science and AI expertise gained from his time as a program manager at Microsoft. The New York City-based Aegis Systems is a venture capital-backed AI startup that provides computer vision software to turn any security camera into a threat-detecting smart camera. Aegis AI system automatically detects firearms in existing security camera feeds, providing early warning and dramatically improving law enforcement response.

Robots perform delicate surgeries, are sent to explore and analyze Martian soil, or accompany elderly patients in their everyday life in the form of friendly baby seal pets. Advances in design and automation of robotic machines have been achieved by understanding and mimicking how living systems evolve, sense, and adapt to their environment. Yet, the range of motions, velocities, and functions of today's robots are still very far from those observed in the living animals such as cephalopods, squeezing through narrow bottlenecks in a few seconds, or even mollusks, able to grind and chew rocks. The quest, however, is not to boldly recreate synthetically living systems but to build machines with a similar level of capabilities in response to specific technological needs. Biotechnology, security or exploration may indeed require autonomous machines with not only well-defined movement accuracy, conformability, actuation speed, but also other characteristics including temperature resistance, mechanical resilience, and optical transparency which may not be found in nature.