A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks

Autonomous agents must learn to collaborate. It is not scalable to develop a new centralized agent every time a task's difficulty outpaces a single agent's abilities. While multi-agent collaboration research has flourished in gridworld-like environments, relatively little work has considered visually rich domains. Addressing this, we introduce the novel task FurnMove in which agents work together to move a piece of furniture through a living room to a goal. Unlike existing tasks, FurnMove requires agents to coordinate at every timestep. We identify two challenges when training agents to complete FurnMove: existing decentralized action sampling procedures do not permit expressive joint action policies and, in tasks requiring close coordination, the number of failed actions dominates successful actions. To confront these challenges we introduce SYNC-policies (synchronize your actions coherently) and CORDIAL (coordination loss). Using SYNC-policies and CORDIAL, our agents achieve a 58% completion rate on FurnMove, an impressive absolute gain of 25 percentage points over competitive decentralized baselines. Our dataset, code, and pretrained models are available at https://unnat.github.io/cordial-sync .Comment: Accepted to ECCV 2020 (spotlight); Project page: https://unnat.github.io/cordial-syn

Planetary Environment Prediction Using Generative Modeling

Planetary rovers have a limited sensory horizon and operate in environments where limited information about the surrounding terrain is available. The rough and unknown nature of the terrain in planetary environments potentially leads to scenarios where the rover gets stuck and has to replan its path frequently to escape such situations. For avoiding such scenarios, we need to exploit spatial knowledge of the environment beyond the rover’s sensor horizon. The solutions presented by existing approaches are limited to indoor environments which are structured. Predicting spatial knowledge for outdoor environments, particularly planetary environments, has not be done before. We attempt to solve planetary environment prediction by exploiting generative learning to (1) learn the distribution of spatial landmarks like rocks and craters which the rover encounter on the planetary surface during exploration and (2) predict spatial landmarks beyond the sensor horizon. We aim to utilize the proposed approach of environment prediction to improve path planning and decision-making processes needed for safe planetary navigation

Learning transferable policies for autonomous planetary landing via deep reinforcement learning

The aim of this work is to develop an application for autonomous landing, exploiting the properties of Deep Reinforcement Learning and Transfer Learning in order to tackle the problem of planetary landing on unknown or barely-known extra-terrestrial environments by learning good-performing policies, which are transferable from the training environment to other, new environments, without losing optimality. To this end, we model a real-physics simulator, by means of the Bullet/PyBullet library, composed by a lander, defined through the standard ROS/URDF framework and realistic 3D terrain models, for which we adapt official NASA 3D meshes, reconstructed from the data retrieved during missions. Where such models are not available, we reconstruct the terrain from mission imagery-generally SAR imagery. In this setup, we train a Deep Reinforcement Learning model-using DDPG and SAC, then comparing the outcomes-to autonomously land on the lunar environment. Moreover, we perform transfer learning on Mars and Titan environments. While still preliminary, our results show that DDPG and SAC can learn good landing policies, that can be transferred to other environments. Good policies can be learned by the SAC algorithm also in the case of atmospheric disturbances-e.g. gusts

Automated End-to-End Workflow for Precise and Geo-accurate Reconstructions using Fiducial Markers

Photogrammetric computer vision systems have been well established in many scientific and commercial fields during the last decades. Recent developments in image-based 3D reconstruction systems in conjunction with the availability of affordable high quality digital consumer grade cameras have resulted in an easy way of creating visually appealing 3D models. However, many of these methods require manual steps in the processing chain and for many photogrammetric applications such as mapping, recurrent topographic surveys or architectural and archaeological 3D documentations, high accuracy in a geo-coordinate system is required which often cannot be guaranteed. Hence, in this paper we present and advocate a fully automated end-to-end workflow for precise and geoaccurate 3D reconstructions using fiducial markers. We integrate an automatic camera calibration and georeferencing method into our image-based reconstruction pipeline based on binary-coded fiducial markers as artificial, individually identifiable landmarks in the scene. Additionally, we facilitate the use of these markers in conjunction with known ground control points (GCP) in the bundle adjustment, and use an online feedback method that allows assessment of the final reconstruction quality in terms of image overlap, ground sampling distance (GSD) and completeness, and thus provides flexibility to adopt the image acquisition strategy already during image recording. An extensive set of experiments is presented which demonstrate the accuracy benefits to obtain a highly accurate and geographically aligned reconstruction with an absolute point position uncertainty of about 1.5 times the ground sampling distance