Ab Initio Particle-based Object Manipulation

This paper presents Particle-based Object Manipulation (Prompt), a new approach to robot manipulation of novel objects ab initio, without prior object models or pre-training on a large object data set. The key element of Prompt is a particle-based object representation, in which each particle represents a point in the object, the local geometric, physical, and other features of the point, and also its relation with other particles. Like the model-based analytic approaches to manipulation, the particle representation enables the robot to reason about the object's geometry and dynamics in order to choose suitable manipulation actions. Like the data-driven approaches, the particle representation is learned online in real-time from visual sensor input, specifically, multi-view RGB images. The particle representation thus connects visual perception with robot control. Prompt combines the benefits of both model-based reasoning and data-driven learning. We show empirically that Prompt successfully handles a variety of everyday objects, some of which are transparent. It handles various manipulation tasks, including grasping, pushing, etc,. Our experiments also show that Prompt outperforms a state-of-the-art data-driven grasping method on the daily objects, even though it does not use any offline training data.Comment: Robotics: Science and Systems (RSS) 202

DiffMimic: Efficient Motion Mimicking with Differentiable Physics

Motion mimicking is a foundational task in physics-based character animation. However, most existing motion mimicking methods are built upon reinforcement learning (RL) and suffer from heavy reward engineering, high variance, and slow convergence with hard explorations. Specifically, they usually take tens of hours or even days of training to mimic a simple motion sequence, resulting in poor scalability. In this work, we leverage differentiable physics simulators (DPS) and propose an efficient motion mimicking method dubbed DiffMimic. Our key insight is that DPS casts a complex policy learning task to a much simpler state matching problem. In particular, DPS learns a stable policy by analytical gradients with ground-truth physical priors hence leading to significantly faster and stabler convergence than RL-based methods. Moreover, to escape from local optima, we utilize a Demonstration Replay mechanism to enable stable gradient backpropagation in a long horizon. Extensive experiments on standard benchmarks show that DiffMimic has a better sample efficiency and time efficiency than existing methods (e.g., DeepMimic). Notably, DiffMimic allows a physically simulated character to learn Backflip after 10 minutes of training and be able to cycle it after 3 hours of training, while the existing approach may require about a day of training to cycle Backflip. More importantly, we hope DiffMimic can benefit more differentiable animation systems with techniques like differentiable clothes simulation in future research.Comment: ICLR 2023 Code is at https://github.com/jiawei-ren/diffmimic Project page is at https://diffmimic.github.io

Ni-Doped Sr\u3csub\u3e2\u3c/sub\u3eFe\u3csub\u3e1.5\u3c/sub\u3eMo\u3csub\u3e0.5\u3c/sub\u3eO\u3csub\u3e6-δ\u3c/sub\u3e as Anode Materials for Solid Oxide Fuel Cells

10% Ni-doped Sr2Fe1.5Mo0.5O6-δ with A-site deficiency is prepared to induce in situ precipitation of B-site metals under anode conditions in solid oxide fuel cells. XRD, SEM and TEM results show that a significant amount of nano-sized Ni-Fe alloy metal phase has precipitated out from Sr1.9Fe1.4Ni0.1Mo0.5O6-δ upon reduction at 800◦C in H2. The conductivity of the reduced composite reaches 29 S cm−1 at 800◦C in H2. Furthermore, fuel cell performance of the composite anode Sr1.9Fe1.4Ni0.1Mo0.5O6-δ-SDC is investigated using H2 as fuel and ambient air as oxidant with La0.8Sr0.2Ga0.87Mg0.13O3 electrolyte and La0.6Sr0.4Co0.2Fe0.8O3 cathode. The cell peak power density reaches 968 mW cm−2 at 800◦C and the voltage is relatively stable under a constant current load of 0.54 A cm−2. After 5 redox cycles of the anode at 800◦C, the fuel cell performance doesn’t suffer any degradation, indicating good redox stability of Sr1.9Fe1.4Ni0.1Mo0.5O6-δ. Peak power density of 227 mW cm−2 was also obtained when propane is used as fuel. These results indicate that a self-generated metal-ceramic composite can been successfully derived from Sr2Fe1.5Mo0.5O6-δ by compositional modifications and Sr1.9Fe1.4Ni0.1Mo0.5O6-δ is a very promising solid oxide fuel cell anode material with enhanced catalytic activity and inherited good redox stability from the parent ceramic material

DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics

Deformable Object Manipulation (DOM) is of significant importance to both daily and industrial applications. Recent successes in differentiable physics simulators allow learning algorithms to train a policy with analytic gradients through environment dynamics, which significantly facilitates the development of DOM algorithms. However, existing DOM benchmarks are either single-object-based or non-differentiable. This leaves the questions of 1) how a task-specific algorithm performs on other tasks and 2) how a differentiable-physics-based algorithm compares with the non-differentiable ones in general. In this work, we present DaXBench, a differentiable DOM benchmark with a wide object and task coverage. DaXBench includes 9 challenging high-fidelity simulated tasks, covering rope, cloth, and liquid manipulation with various difficulty levels. To better understand the performance of general algorithms on different DOM tasks, we conduct comprehensive experiments over representative DOM methods, ranging from planning to imitation learning and reinforcement learning. In addition, we provide careful empirical studies of existing decision-making algorithms based on differentiable physics, and discuss their limitations, as well as potential future directions.Comment: ICLR 2023 (Oral

Genome sequence of the insect pathogenic fungus Cordyceps militaris, a valued traditional chinese medicine

Species in the ascomycete fungal genus Cordyceps have been proposed to be the teleomorphs of Metarhizium species. The latter have been widely used as insect biocontrol agents. Cordyceps species are highly prized for use in traditional Chinese medicines, but the genes responsible for biosynthesis of bioactive components, insect pathogenicity and the control of sexuality and fruiting have not been determined. Here, we report the genome sequence of the type species Cordyceps militaris. Phylogenomic analysis suggests that different species in the Cordyceps/Metarhizium genera have evolved into insect pathogens independently of each other, and that their similar large secretomes and gene family expansions are due to convergent evolution. However, relative to other fungi, including Metarhizium spp., many protein families are reduced in C. militaris, which suggests a more restricted ecology. Consistent with its long track record of safe usage as a medicine, the Cordyceps genome does not contain genes for known human mycotoxins. We establish that C. militaris is sexually heterothallic but, very unusually, fruiting can occur without an opposite mating-type partner. Transcriptional profiling indicates that fruiting involves induction of the Zn2Cys6-type transcription factors and MAPK pathway; unlike other fungi, however, the PKA pathway is not activated.https://doi.org/10.1186/gb-2011-12-11-r11