We aim to solve the problem of manipulating deformable objects, particularly elastic bands, in real-world scenarios. However, deformable object manipulation (DOM) requires a policy that works on a large state space due to the unlimited degree of freedom (DoF) of deformable objects. Further, their dense but partial observations (e.g., images or point clouds) may increase the sampling complexity and uncertainty in policy learning. To figure it out, we propose a novel implicit neural-representation (INR) learning for elastic DOMs, called INR-DOM. Our method learns consistent state representations associated with partially observable elastic objects reconstructing a complete and implicit surface represented as a signed distance function. Furthermore, we perform exploratory representation fine-tuning through reinforcement learning (RL) that enables RL algorithms to effectively learn exploitable representations while efficiently obtaining a DOM policy. We perform quantitative and qualitative analyses building three simulated environments and real-world manipulation studies with a Franka Emika Panda arm. Videos are available at http://inr-dom.github.io.

Programming physical intelligence into mechanisms holds great promise for machines that can accomplish tasks such as navigation of unstructured environments while utilizing a minimal amount of computational resources and electronic components. In this study, we introduce a novel design approach for physically intelligent under-actuated mechanisms capable of autonomously adjusting their motion in response to environmental interactions. Specifically, multistability is harnessed to sequence the motion of different degrees of freedom in a programmed order. A key aspect of this approach is that these sequences can be passively reprogrammed through mechanical stimuli that arise from interactions with the environment. To showcase our approach, we construct a four degree of freedom robot capable of autonomously navigating mazes and moving away from obstacles. Remarkably, this robot operates without relying on traditional computational architectures and utilizes only a single linear actuator.

This work briefly covers our efforts to stabilize the flight dynamics of Northeastern's tailless bat-inspired micro aerial vehicle, Aerobat. Flapping robots are not new. A plethora of examples is mainly dominated by insect-style design paradigms that are passively stable. However, Aerobat, in addition for being tailless, possesses morphing wings that add to the inherent complexity of flight control. The robot can dynamically adjust its wing platform configurations during gait cycles, increasing its efficiency and agility. We employ a guard design with manifold small thrusters to stabilize Aerobat's position and orientation in hovering, a flapping system in tandem with a multi-rotor. For flight control purposes, we take an approach based on assuming the guard cannot observe Aerobat's states. Then, we propose an observer to estimate the unknown states of the guard which are then used for closed-loop hovering control of the Guard-Aerobat platform.

Large language models (LLMs) such as ChatGPT and GPT-4 have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain point for LLMs. It remains unclear that: (1) Can GPTs effectively answer commonsense questions? (2) Are GPTs knowledgeable in commonsense? (3) Are GPTs aware of the underlying commonsense knowledge for answering a specific question? (4) Can GPTs effectively leverage commonsense for answering questions? To evaluate the above commonsense problems, we conduct a series of experiments to evaluate ChatGPT's commonsense abilities, and the experimental results show that: (1) GPTs can achieve good QA accuracy in commonsense tasks, while they still struggle with certain types of knowledge. (2) ChatGPT is knowledgeable, and can accurately generate most of the commonsense knowledge using knowledge prompts. (3) Despite its knowledge, ChatGPT is an inexperienced commonsense problem solver, which cannot precisely identify the needed commonsense knowledge for answering a specific question, i.e., ChatGPT does not precisely know what commonsense knowledge is required to answer a question. The above findings raise the need to investigate better mechanisms for utilizing commonsense knowledge in LLMs, such as instruction following, better commonsense guidance, etc.

In this paper, we introduce a data-driven modeling approach for dynamics problems with latent variables. The state-space of the proposed model includes artificial latent variables, in addition to observed variables that can be fitted to a given data set. We present a model framework where the stability of the coupled dynamics can be easily enforced. The model is implemented by recurrent cells and trained using backpropagation through time. Numerical examples using benchmark tests from order reduction problems demonstrate the stability of the model and the efficiency of the recurrent cell implementation. As applications, two fluid-structure interaction problems are considered to illustrate the accuracy and predictive capability of the model.

Imagine visiting the control room of a metals company. You're there to discuss asset performance and process optimization. During the visit, you see on a desk a computer mouse wrapped in a rubber band. On the nearby computer screen, the cursor hovers over an icon that a person would click to acknowledge an alarm triggered by the automated system tracking the thousands of sensors placed throughout the company's facilities. It seems it would never be clear to the person sitting in that chair if there was a serious problem or not.

Scientists have developed a fibre that combines the elasticity of rubber with the strength of a metal. Researchers at North Carolina State University are behind the innovation, which has created a tougher material that could be incorporated into soft robotics, packaging materials or next-generation textiles. The team made fibres consisting of a gallium metal core surrounded by an elastic polymer sheath. When placed under stress, the fibre has the strength of the metal core. But whereas the metal eventually breaks, the fiber doesn't fail - the polymer sheath absorbs the strain between the breaks in the metal and transfers the stress back to the metal core.

Released in April, Nintendo Labo was one of the more unusual games of this year – or any year. The box contains cardboard sheets, rubber bands and string along with a game cartridge, inviting players to build ingenious little cardboard models that, when combined with the Nintendo Switch console and its controllers, become working interactive toys. It's rather like cardboard Lego, presented in a way that gently introduces the basics of engineering. Labo is not as playground-popular as Minecraft or Fortnite, but it's a rare video game that provides educational value as well as fun, and does so without forcing it down kids' throats. There are three Nintendo Labo sets available: the Variety Kit, the Robot Kit and the Vehicles Kit.

Researchers have developed a type of walking'bio-bot' powered by skeletal muscle cells. The bots muscle cells can be controlled by blue light, causing the multi-legged bot to move forwards. The team that designed the bio-bot released a step-by-step guide of how to make one so that other researchers have the knowledge to build their own. Schematic of a bio-bot: Engineered skeletal muscle tissue is coupled to a 3D printed flexible skeleton. The researchers, based at the University of Illinois at Urbana-Champaign, developed the robot using 3D printing to make a skeleton for the bot.

Heboi is a Japanese word that loosely means something is technically poor, or crappy. Thus: Hebocon, which, according to the founders, extends to both the robots and the people that make them -- but in an affectionate, pat-on-the-back kind of way. The competition involves several sumo-style matches in which the robots try to push their opponents out of the arena. It's no DARPA challenge, but when you see the robots in action you begin to realize it's almost as difficult a struggle. Most of the robots on show at Hebocon can't be controlled very well.