Aerial vehicles are revolutionizing applications that require capturing the 3D structure of dynamic targets in the wild, such as sports, medicine and entertainment. The core challenges in developing a motion-capture system that operates in outdoors environments are: (1) 3D inference requires multiple simultaneous viewpoints of the target, (2) occlusion caused by obstacles is frequent when tracking moving targets, and (3) the camera and vehicle state estimation is noisy. We present a real-time aerial system for multi-camera control that can reconstruct human motions in natural environments without the use of special-purpose markers. We develop a multi-robot coordination scheme that maintains the optimal flight formation for target reconstruction quality amongst obstacles. We provide studies evaluating system performance in simulation, and validate real-world performance using two drones while a target performs activities such as jogging and playing soccer. Supplementary video: https://youtu.be/jxt91vx0cns

Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces.In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state.Our key insight is to train language models on human gameplay, where people demonstrate linguistic priors and a general game sense for promising actions conditioned on game history.We combine CALM with a reinforcement learning agent which re-ranks the generated action candidates to maximize ingame rewards.We evaluate our approach using the Jericho benchmark (Hausknecht et al., 2019a), on games unseen by CALM during training.Our method obtains a 69% relative improvement in average game score over the previous state-of-the-art model.Surprisingly, on half of these games, CALM is competitive with or better than other models that have access to ground truth admissible actions.* * Code and data are available at https://github. com/princeton-nlp/calm-textgame.Observation: You are in the living room.There is a doorway to the east, a wooden door with strange gothic lettering to the west, which appears to be nailed shut, a trophy case, and a large oriental rug in the center of the room.You are carrying: A brass lantern . . .