Models that are learned from real-world data are often biased because the data usedtotrainthemisbiased.

Ourapproach, Exploration through Learned Language Abstraction (ELLA) providesintermediate rewards to an agent for completing relevant low-level behaviors as it tries to solve a complex, sparse rewardtask.

Colorsdesignatesupervision used: shadesofblue = highleveladvice; red = lowleveladvice; black = oracledemonstrations; gray = shaped rewards. Figure 6: "Bestadvice" is OffsetAdvice.

A Long-Duration Autonomy Approach to Connected and Automated V ehicles Logan E. Beaver, Member, IEEE Abstract --In this article, we present a long-duration autonomy approach for the control of connected and automated vehicles (CA Vs) operating in a transportation network. In particular, we focus on the performance of CA Vs at traffic bottlenecks, including roundabouts, merging roadways, and intersections. We take a principled approach based on optimal control, and derive a reactive controller with guarantees on safety, performance, and energy efficiency. We guarantee safety through high order control barrier functions (HOCBFs), which we "lift" to first order CBFs using time-optimal motion primitives. We demonstrate the performance of our approach in simulation and compare it to an optimal control-based approach. Index T erms --autonomous systems, connected vehicles, long-duration autonomy, barrier functions I. I NTRODUCTION C ONNECTED and automated vehicles (CA Vs) continue to proliferate transportation networks. As a result, it is critical for us to develop control algorithms that are computationally efficient, provably safe, and produce energy-efficient trajectories.

Realism is a big draw for fans of management sims, and both EA FC and Football Manager have kept salaries and transfer fees for women players in line with the real world, where amounts tend to be much higher in the men's game. But Andreas says the EA FC team has made a few concessions - such as the venues where matches in the game take place. Using his home club Frankfurt as an example, he says the women's squad usually plays at a smaller stadium but all the matches in EA FC take place in the Deutsche Bank Park. It's the largest stadium in the German city, where the ladies team's bigger fixtures are held in real-life. On a practical level, it means the team doesn't need to recreate another venue inside the game.

In tasks like semantic parsing, instruction following, and question answering, standard deep networks fail to generalize compositionally from small datasets. Many existing approaches overcome this limitation with model architectures that enforce a compositional process of sentence interpretation. In this paper, we present a domain-general and model-agnostic formulation of compositionality as a constraint on symmetries of data distributions rather than models. Informally, we prove that whenever a task can be solved by a compositional model, there is a corresponding data augmentation scheme -- a procedure for transforming examples into other well formed examples -- that imparts compositional inductive bias on any model trained to solve the same task. We describe a procedure called LEXSYM that discovers these transformations automatically, then applies them to training data for ordinary neural sequence models. Unlike existing compositional data augmentation procedures, LEXSYM can be deployed agnostically across text, structured data, and even images. It matches or surpasses state-of-the-art, task-specific models on COGS semantic parsing, SCAN and ALCHEMY instruction following, and CLEVR-COGENT visual question answering datasets.

Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization. One of the current de-facto solutions to this problem is compositional data augmentation, aiming to incur additional compositional inductive bias. Nonetheless, the improvement offered by existing handcrafted augmentation strategies is limited when successful systematic generalization of neural sequence models requires multi-grained compositional bias (i.e., not limited to either lexical or structural biases only) or differentiation of training sequences in an imbalanced difficulty distribution. To address the two challenges, we first propose a novel compositional augmentation strategy dubbed \textbf{Span} \textbf{Sub}stitution (SpanSub) that enables multi-grained composition of substantial substructures in the whole training set. Over and above that, we introduce the \textbf{L}earning \textbf{to} \textbf{S}ubstitute \textbf{S}pan (L2S2) framework which empowers the learning of span substitution probabilities in SpanSub in an end-to-end manner by maximizing the loss of neural sequence models, so as to outweigh those challenging compositions with elusive concepts and novel surroundings. Our empirical results on three standard compositional generalization benchmarks, including SCAN, COGS and GeoQuery (with an improvement of at most 66.5\%, 10.3\%, 1.2\%, respectively), demonstrate the superiority of SpanSub, %the learning framework L2S2 and their combination.

Machine learning used to be an academic pursuit. If you wanted to work on it, you probably needed to be part of a lab or have a PhD. In early 2021, there was a shift. RiversHaveWings followed up with the VQGAN CLIP notebook. These notebooks turned text descriptions into images by guiding a GAN with CLIP.

Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. In this work, we analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias (i.e., a source sequence already mapped to a target sequence is less likely to be mapped to other target sequences), and the tendency to memorize whole examples rather than separating structures from contents. We propose two techniques to address these two issues respectively: Mutual Exclusivity Training that prevents the model from producing seen generations when facing novel, unseen examples via an unlikelihood-based loss; and prim2primX data augmentation that automatically diversifies the arguments of every syntactic function to prevent memorizing and provide a compositional inductive bias without exposing test-set data. Combining these two techniques, we show substantial empirical improvements using standard sequence-to-sequence models (LSTMs and Transformers) on two widely-used compositionality datasets: SCAN and COGS. Finally, we provide analysis characterizing the improvements as well as the remaining challenges, and provide detailed ablations of our method. Our code is available at https://github.com/owenzx/met-primaug

The study of robotic flocking has received significant attention in the past twenty years. In this article, we present a constraint-driven control algorithm that minimizes the energy consumption of individual agents and yields an emergent V formation. As the formation emerges from the decentralized interaction between agents, our approach is robust to the spontaneous addition or removal of agents to the system. First, we present an analytical model for the trailing upwash behind a fixed-wing UAV, and we derive the optimal air speed for trailing UAVs to maximize their travel endurance. Next, we prove that simply flying at the optimal airspeed will never lead to emergent flocking behavior, and we propose a new decentralized "anseroid" behavior that yields emergent V formations. We encode these behaviors in a constraint-driven control algorithm that minimizes the locomotive power of each UAV. Finally, we prove that UAVs initialized in an approximate V or echelon formation will converge under our proposed control law, and we demonstrate this emergence occurs in real-time in simulation and in physical experiments with a fleet of Crazyflie quadrotors.