Extreme Parkour with Legged Robots

Humans can perform parkour by traversing obstacles in a highly dynamic fashion requiring precise eye-muscle coordination and movement. Getting robots to do the same task requires overcoming similar challenges. Classically, this is done by independently engineering perception, actuation, and control systems to very low tolerances. This restricts them to tightly controlled settings such as a predetermined obstacle course in labs. In contrast, humans are able to learn parkour through practice without significantly changing their underlying biology. In this paper, we take a similar approach to developing robot parkour on a small low-cost robot with imprecise actuation and a single front-facing depth camera for perception which is low-frequency, jittery, and prone to artifacts. We show how a single neural net policy operating directly from a camera image, trained in simulation with large-scale RL, can overcome imprecise sensing and actuation to output highly precise control behavior end-to-end. We show our robot can perform a high jump on obstacles 2x its height, long jump across gaps 2x its length, do a handstand and run across tilted ramps, and generalize to novel obstacle courses with different physical properties. Parkour videos at https://extreme-parkour.github.io/Comment: Website and videos at https://extreme-parkour.github.io

Soft BPR Loss for Dynamic Hard Negative Sampling in Recommender Systems

In recommender systems, leveraging Graph Neural Networks (GNNs) to formulate the bipartite relation between users and items is a promising way. However, powerful negative sampling methods that is adapted to GNN-based recommenders still requires a lot of efforts. One critical gap is that it is rather tough to distinguish real negatives from massive unobserved items during hard negative sampling. Towards this problem, this paper develops a novel hard negative sampling method for GNN-based recommendation systems by simply reformulating the loss function. We conduct various experiments on three datasets, demonstrating that the method proposed outperforms a set of state-of-the-art benchmarks.Comment: 9 pages, 16 figure

Learning Perception-Aware Agile Flight in Cluttered Environments

Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously navigating quadrotors through cluttered environments in minimum time. However, they are not perception aware, a crucial requirement in vision-based navigation due to the camera's limited field of view and the underactuated nature of a quadrotor. We propose a learning-based system that achieves perception-aware, agile flight in cluttered environments. Our method combines imitation learning with reinforcement learning (RL) by leveraging a privileged learning-by-cheating framework. Using RL, we first train a perception-aware teacher policy with full-state information to fly in minimum time through cluttered environments. Then, we use imitation learning to distill its knowledge into a vision-based student policy that only perceives the environment via a camera. Our approach tightly couples perception and control, showing a significant advantage in computation speed (10×faster) and success rate. We demonstrate the closed-loop control performance using hardware-in-the-loop simulation

Deep Imaging of the HCG 95 Field.I.Ultra-diffuse Galaxies

We present a detection of 89 candidates of ultra-diffuse galaxies (UDGs) in a 4.9 degree$^2$ field centered on the Hickson Compact Group 95 (HCG 95) using deep $g$- and $r$-band images taken with the Chinese Near Object Survey Telescope. This field contains one rich galaxy cluster (Abell 2588 at $z$=0.199) and two poor clusters (Pegasus I at $z$=0.013 and Pegasus II at $z$=0.040). The 89 candidates are likely associated with the two poor clusters, giving about 50 $-$ 60 true UDGs with a half-light radius $r_{\rm e} > 1.5$ kpc and a central surface brightness $\mu(g,0) > 24.0$ mag arcsec$^{-2}$. Deep $z$'-band images are available for 84 of the 89 galaxies from the Dark Energy Camera Legacy Survey (DECaLS), confirming that these galaxies have an extremely low central surface brightness. Moreover, our UDG candidates are spread over a wide range in $g-r$ color, and $\sim$26% are as blue as normal star-forming galaxies, which is suggestive of young UDGs that are still in formation. Interestingly, we find that one UDG linked with HCG 95 is a gas-rich galaxy with H I mass $1.1 \times 10^{9} M_{\odot}$ detected by the Very Large Array, and has a stellar mass of $M_\star \sim 1.8 \times 10^{8}$ $M_{\odot}$. This indicates that UDGs at least partially overlap with the population of nearly dark galaxies found in deep H I surveys. Our results show that the high abundance of blue UDGs in the HCG 95 field is favored by the environment of poor galaxy clusters residing in H I-rich large-scale structures.Comment: Published in Ap

Communication-Efficient Topologies for Decentralized Learning with O(1)O(1) Consensus Rate

Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server. Since communication tends to be slower than computation, when each agent communicates with only a few neighboring agents per iteration, they can complete iterations faster than with more agents or a central server. However, the total number of iterations to reach a network-wide solution is affected by the speed at which the agents' information is ``mixed'' by communication. We found that popular communication topologies either have large maximum degrees (such as stars and complete graphs) or are ineffective at mixing information (such as rings and grids). To address this problem, we propose a new family of topologies, EquiTopo, which has an (almost) constant degree and a network-size-independent consensus rate that is used to measure the mixing efficiency. In the proposed family, EquiStatic has a degree of $\Theta(\ln(n))$, where $n$ is the network size, and a series of time-dependent one-peer topologies, EquiDyn, has a constant degree of 1. We generate EquiDyn through a certain random sampling procedure. Both of them achieve an $n$-independent consensus rate. We apply them to decentralized SGD and decentralized gradient tracking and obtain faster communication and better convergence, theoretically and empirically. Our code is implemented through BlueFog and available at \url{https://github.com/kexinjinnn/EquiTopo}Comment: NeurIPS 202

Temporal heterogeneity of the root microbiome in Panax ginseng soils across ecological compartments under mild soil disturbance

IntroductionKnowledge on spatiotemporal heterogeneity of plant root microbiomes is lacking. The diversity of the root microbiome must be revealed for understanding plant–microbe interactions and the regulation of functionally crucial microbial taxa.MethodsWe here investigated the dynamics of microbial group characteristics within each soil ecological compartment [rhizoplane (B), rhizosphere (J), and bulk soil (T)] across different cultivation years (year 4: F4 and year 5: F5) by using high-throughput sequencing (16S and ITS).ResultsAccording to the species diversity, microbiome diversity and the ASV (amplified sequence variant) number in the rhizoplane ecotone increased significantly with an increase in the planting years. By contrast, the microbiome diversity of the rhizosphere soil remained relatively stable. PCoA and PERMANOVA analyses revealed that microbial taxa among different planting years and ecological compartments varied significantly. Planting years exerted the least effect on the rhizosphere microbiome, but their impact on fungi in the rhizoplane and bacteria in the bulk soil was the most significant.DiscussionPlanting years influenced the microbial community composition in various ecological compartments of ginseng root soil. Potentially harmful fungi such as Cryptococcus (2.83%), Neonectria (0.89%), llyonectria (0.56%), Gibberella (0.41%), Piloderma (4.44%), and Plectosphaerella (3.88%) were enriched in F5B with an increase in planting years, whereas the abundance of potentially beneficial Mortierella increased. Correlation analysis indicated associations between bacterial taxa and soil pH/S-CAT, and between fungal taxa and soil moisture content/total potassium. Our study highlights the significance of changes in rhizoplane fungi and the stability of the rhizosphere microbial community in comprehending plant ecological sustainability

Construction of Visual Inspection Database for Catenary on High-speed Railways

With the rapid development of computer vision, techniques of machine vision and visual inspection have been applied into the inspection of catenary on high-speed railways. Visual inspection systems have been developed and super-high-resolution images are captured to check the status of catenary components. Automatic recognition of defects becomes very important since the number of images is too huge to be manually checked one by one. However, it is not easy for the development of recognition algorithms on catenary components. There are many types of defects to be checked on different kinds of catenary components, but the number of defect images is too small in real world. In this paper, a solution was proposed and implemented. An on-site data acquisition system was designed and developed, and different types of defects were manually made on different catenary components beforehand. Finally, a visual inspection database was successfully constructed, including plenty of different kinds of catenary components, different types of defects, in different inspection conditions. The visual inspection database will be of great use in the development and test of recognition algorithms for catenary

Towards Finding the Best Characteristics of Some Bit-oriented Block Ciphers and Automatic Enumeration of (Related-key) Differential and Linear Characteristics with Predefined Properties

In this paper, we investigate the Mixed-integer Linear Programming (MILP) modelling of the differential and linear behavior of a wide range of block ciphers. We point out that the differential behavior of an arbitrary S-box can be exactly described by a small system of linear inequalities. ~~~~~Based on this observation and MILP technique, we propose an automatic method for finding high probability (related-key) differential or linear characteristics of block ciphers. Compared with Sun {\it et al.}\u27s {\it heuristic} method presented in Asiacrypt 2014, the new method is {\it exact} for most ciphers in the sense that every feasible 0-1 solution of the MILP model generated by the new method corresponds to a valid characteristic, and therefore there is no need to repeatedly add valid cutting-off inequalities into the MILP model as is done in Sun {\it et al.}\u27s method; the new method is more powerful which allows us to get the {\it exact lower bounds} of the number of differentially or linearly active S-boxes; and the new method is more efficient which allows to obtain characteristic with higher probability or covering more rounds of a cipher (sometimes with less computational effort). ~~~~~Further, by encoding the probability information of the differentials of an S-boxes into its differential patterns, we present a novel MILP modelling technique which can be used to search for the characteristics with the maximal probability, rather than the characteristics with the smallest number of active S-boxes. With this technique, we are able to get tighter security bounds and find better characteristics. ~~~~~Moreover, by employing a type of specially constructed linear inequalities which can remove {\it exactly one} feasible 0-1 solution from the feasible region of an MILP problem, we propose a method for automatic enumeration of {\it all} (related-key) differential or linear characteristics with some predefined properties, {\it e.g.}, characteristics with given input or/and output difference/mask, or with a limited number of active S-boxes. Such a method is very useful in the automatic (related-key) differential analysis, truncated (related-key) differential analysis, linear hull analysis, and the automatic construction of (related-key) boomerang/rectangle distinguishers. ~~~~~The methods presented in this paper are very simple and straightforward, based on which we implement a Python framework for automatic cryptanalysis, and extensive experiments are performed using this framework. To demonstrate the usefulness of these methods, we apply them to SIMON, PRESENT, Serpent, LBlock, DESL, and we obtain some improved cryptanalytic results