Multi-robot systems in complex physical collaborations face a "shared brain dilemma": transmitting high-dimensional multimedia data (e.g., video streams at ~30MB/s) creates severe bandwidth bottlenecks and decision-making latency. To address this, we propose PIPHEN, an innovative distributed physical cognition-control framework. Its core idea is to replace "raw data communication" with "semantic communication" by performing "semantic distillation" at the robot edge, reconstructing high-dimensional perceptual data into compact, structured physical representations. This idea is primarily realized through two key components: (1) a novel Physical Interaction Prediction Network (PIPN), derived from large model knowledge distillation, to generate this representation; and (2) a Hamiltonian Energy Network (HEN) controller, based on energy conservation, to precisely translate this representation into coordinated actions. Experiments show that, compared to baseline methods, PIPHEN can compress the information representation to less than 5% of the original data volume and reduce collaborative decision-making latency from 315ms to 76ms, while significantly improving task success rates. This work provides a fundamentally efficient paradigm for resolving the "shared brain dilemma" in resource-constrained multi-robot systems.

THE NEW HORIZONS SPACE PROBE carried 30 grams of Clyde Tombaugh's ashes to commemorate Tombaugh's discovery of Pluto in 1930. The package is no different in its information content than 30 grams of cigarette ashes, since Tombaugh's unique genetic information was burnt up and never loaded on board. A proper celebration of Tombaugh's historic contribution could have taken the form of an electronic record of his DNA. The Principal Investigator of the mission, Alan Stern, told me that sending a stem cell with Tombaugh's genetic making would have triggered a bureaucratic nightmare at NASA. The challenge is even bigger for launching a complete human being in the form of an astronaut to a journey in space that lasts more than a few years.

We study the problem of learning physical object representations for robot manipulation. Understanding object physics is critical for successful object manipulation, but also challenging because physical object properties can rarely be inferred from the object's static appearance. In this paper, we propose DensePhysNet, a system that actively executes a sequence of dynamic interactions (e.g., sliding and colliding), and uses a deep predictive model over its visual observations to learn dense, pixel-wise representations that reflect the physical properties of observed objects. Our experiments in both simulation and real settings demonstrate that the learned representations carry rich physical information, and can directly be used to decode physical object properties such as friction and mass. The use of dense representation enables DensePhysNet to generalize well to novel scenes with more objects than in training. With knowledge of object physics, the learned representation also leads to more accurate and efficient manipulation in downstream tasks than the state-of-the-art.

Leibniz's (1984) An Introduction to a Secret Encyclopedia includes the following marginal note: Principle of Physical Certainty: Everything which men have experienced always and in many ways will still happen: for example that iron sinks in water (Leibniz 1984). In our daily lives, we routinely use this principle. Thus, we know that we can pull with a string but not push with it; that a flower pot dropped from our balcony falls to the ground and breaks; that when we place a container of water on fire, water might boil after a while and overflow the container. The origin of such knowledge is a matter of constant debate. It is clear that we learn a great deal about the physical world as we grow up.

Jim Saveland For a fire in that fuel complex to Research Forester The Phoenix project ("Trial by Fire: grow to the size indicated in the time Associate Editor, AI Application in Understanding the Design Requirements indicated would require a midflame Natural Resource Management for Agents in Complex Environments." Agriculture 3) presents very interesting work in The authors go on to state, "Firefighting Forest Service forest fire simulation. I am especially objects are also accurately Southern Forest Fire Laboratory glad to see recognition that the "realtime, simulated; for example, bulldozers Route 1, Box 182A spatially distributed, multiagent, move at a maximum speed of... 0.5 Dry Branch, GA 31020 dynamic, and unpredictable fire kph when cutting a fireline." In reality, environment" provides an excellent sustained fireline production for Editor: opportunity to explore a variety of AI bulldozers is variable (0.1 - 2.0 kph) issues, such as how complex environments depending on steepness of the slope, Mr. Saveland's letter focuses our constrain the design of intelligent vegetation, and size of the bulldozer. I hope more AI researchers Furthermore, although bulldozers are between accuracy and realism.

Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta (Larkin 1983). We give several examples showing the power of physical representations.

Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta (Larkin 1983). We give several examples showing the power of physical representations.