Search for: All records

Tensegrity robots are composed of rigid struts and flexible cables. They constitute an emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide array of applications, ranging from locomotion to assembly. They are difficult to control and model accurately, however, due to their compliance and high number of degrees of freedom. To address this issue, prior work has introduced a differentiable physics engine designed for tensegrity robots based on first principles. In contrast, this work proposes the use of graph neural networks to model contact dynamics over a graph representation of tensegrity robots, which leverages their natural graph-like cable connectivity between end caps of rigid rods. This learned simulator can accurately model 3-bar and 6-bar tensegrity robot dynamics in simulation-to-simulation experiments where MuJoCo is used as the ground truth. It can also achieve higher accuracy than the previous differentiable engine for a real 3-bar tensegrity robot, for which the robot state is only partially observable. When compared against direct applications of recent mesh-based graph neural network simulators, the proposed approach is computationally more efficient, both for training and inference, while achieving higher accuracy. Code and data are available at https://github.com/nchen9191/tensegrity_gnn_simulator_public 

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. Physics-based simulation is a promising avenue for developing locomotion policies that can be transferred to real robots. Nevertheless, modeling tensegrity robots is a complex task due to a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy for tensegrity robots. This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot. These data include offline measurements of physical properties, such as mass and geometry for various robot components, and the observation of a trajectory using a random control policy. With the data from the real robot, the engine can be iteratively refined and used to discover locomotion policies that are directly transferable to the real robot. Beyond the R2S2R pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function for matching tensegrity locomotion gaits, and a trajectory segmentation technique that avoids conflicts in gradient evaluation during training. Multiple iterations of the R2S2R process are demonstrated and evaluated on a real 3-bar tensegrity robot. 

To obtain thorough understandings of precipitation process in heat-treatable Mg-Ca-Zn alloy, we revisited the precipitation process of a Mg-0.3Ca-0.6 Zn (at.%) dilute alloy during isothermal aging at 200 °C using an aberration-corrected scanning transmission electron microscope, atom probe tomography, and first-principles calculations. The monolayer G.P. zones form on the (0002)α plane in the peak-aged condition and transform into tri-atomic layer η'' and η' plates with a thickness of a single unit-cell height. The η' plates, then, form in pairs and stacks with energetically favorable 4–5 atomic layers of pure magnesium between the plates. While such a transformation path is similar to that seen in Mg-RE-Zn alloys (RE: rare-earth elements), the unique structure of coarse η1 plates that precipitate after the η' plates leads to a different precipitate microstructure evolution from the Mg-RE-Zn system. The η1 phase (Mg7Ca2Zn3) is unevenly distributed in the matrix after 100 h of aging and finally evolves to the equilibrium η phase (Mg10Ca3Zn6) phase with a hexagonal structure. First-principles calculations of energetics were performed to further identify the crystal structure and stability of the precipitates, supporting the following new precipitation sequence: S.S.S.S. → G.P. zones → η'' → η' → η' pairs and stacks / η1 → η 

We report on a total of 310 samples from marine sediments drilled in the Indian Ocean that were analyzed for glass shard compositions. Samples are mainly from International Ocean Discovery Program Expeditions 353 and 362 but are complemented by samples from Expedition 354; Ocean Drilling Program Legs 183, 121, 120, 119, 116, and 115; and Deep Sea Drilling Project Leg 22. We performed 4327 successful single glass shard analyses with the electron microprobe for major element compositions and conducted 937 successful single analyses with laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) for trace element compositions on individual glass shards previously measured with the electron microprobe. In total, we were able to measure glass compositions for 254 samples. Of all the samples, 235 can be classified as tephra layers containing pyroclasts as the predominant component in their clast inventory between the 63 and 125 µm grain size fraction, often exceeding 90 vol%. The compositions of the Indian Ocean marine tephras range from basalt to rhyolite and from basaltic trachyandesite to trachyte and fall into the calc-alkaline, K-rich calc-alkaline, and shoshonitic magmatic series. 

The evolutionary sequence for high-mass star formation starts with massive starless clumps that go on to form protostellar, young stellar objects and then compact H ii regions. While there are many examples of the three later stages, the very early stages have proved to be elusive. We follow-up a sample of 110 mid-infrared dark clumps selected from the ATLASGAL catalogue with the IRAM telescope in an effort to identify a robust sample of massive starless clumps. We have used the HCO+ and HNC (1-0) transitions to identify clumps associated with infall motion and the SiO (2-1) transition to identity outflow candidates. We have found blue asymmetric line profile in 65 per cent of the sample, and have measured the infall velocities and mass infall rates (0.6–36 × 10−3 M⊙ yr−1) for 33 of these clumps. We find a trend for the mass infall rate decreasing with an increase of bolometric luminosity to clump mass, i.e. star formation within the clumps evolves. Using the SiO 2-1 line, we have identified good outflow candidates. Combining the infall and outflow tracers reveals that 67 per cent of quiescent clumps are already undergoing gravitational collapse or are associated with star formation; these clumps provide us with our best opportunity to determine the initial conditions and study the earliest stages of massive star formation. Finally, we provide an overview of a systematic high-resolution ALMA study of quiescent clumps selected that allows us to develop a detailed understanding of earliest stages and their subsequent evolution.