We present a method for synthesizing navigation policies for multi-robot teams that interpret and follow natural language instructions. We condition these policies on embeddings from pretrained Large Language Models (LLMs), and train them via offline reinforcement learning with as little as 20 minutes of randomly-collected real-world data. Experiments on a team of five real robots show that these policies generalize well to unseen commands, indicating an understanding of the LLM latent space. Our method requires no simulators or environment models, and produces low-latency control policies that can be deployed directly to real robots without finetuning. We provide videos of our experiments at https://sites.google.com/view/llm-marl.

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors.

Evolutionary science provides evidence that diversity confers resilience in natural systems. Yet, traditional multi-agent reinforcement learning techniques commonly enforce homogeneity to increase training sample efficiency. When a system of learning agents is not constrained to homogeneous policies, individuals may develop diverse behaviors, resulting in emergent complementarity that benefits the system. Despite this, there is a surprising lack of tools that quantify behavioral diversity. Such techniques would pave the way towards understanding the impact of diversity in collective artificial intelligence and enabling its control. In this paper, we introduce System Neural Diversity (SND): a measure of behavioral heterogeneity in multi-agent systems. We discuss and prove its theoretical properties, and compare it with alternate, state-of-the-art behavioral diversity metrics used in the robotics domain. Through simulations of a variety of cooperative multi-robot tasks, we show how our metric constitutes an important tool that enables measurement and control of behavioral heterogeneity. In dynamic tasks, where the problem is affected by repeated disturbances during training, we show that SND allows us to measure latent resilience skills acquired by the agents, while other proxies, such as task performance (reward), fail to. Finally, we show how the metric can be employed to control diversity, allowing us to enforce a desired heterogeneity set-point or range. We demonstrate how this paradigm can be used to bootstrap the exploration phase, finding optimal policies faster, thus enabling novel and more efficient MARL paradigms.

This paper proposes an ANN-based framework for forecasting the daily solar irradiance with the meteorological data collected over 10 years for Hisar city, located in Haryana. Meteorological information like temperature, relative humidity, and average wind speed is used as input variables for estimating sun irradiation in order to obtain high accuracy under various weather situations. Meteorological information is used as input variables for estimating the solar irradiation in order to obtain acceptable accuracy in a variety of weather circumstances. In addition, it was suggested in this paper that the Artificial Neural Network (ANN) model be trained using the Harris Hawks Optimization (HHO) Algorithm. The following task is to choose the best solar panel (SP) out of the thousands that are currently on the market. Due to a crucial component like weather, choosing the finest SP is important. Hence, this paper introduces a score-based model for selecting the best panel by considering the different criteria of the eleven SPs that are used as test panels. The annual power produced by panel P11 is 79.67% better than panel P1. Finally, based on the average power generated, the proposed panel P11 is regarded as the best panel when compared to other panels.

Several independent approaches exist for state estimation and control of multirotor unmanned aerial systems (UASs) that address specific and constrained operational conditions. This work presents a complete end-to-end pipeline that enables precise, aggressive and agile maneuvers for multirotor UASs under real and challenging outdoor environments. We leverage state-of-the-art optimal methods from the literature for trajectory planning and control, such that designing and executing dynamic paths is fast, robust and easy to customize for a particular application. The complete pipeline, built entirely using commercially available components, is made open-source and fully documented to facilitate adoption. We demonstrate its performance in a variety of operational settings, such as hovering at a spot under dynamic wind speeds of up to 5– 6m/s (12–15mi/h) while staying within 12cm of 3D error. We also characterize its capabilities in flying high-speed trajectories outdoors, and enabling fast aerial docking with a moving target with planning and interception occurring in under 8s.

Sensor payloads suspended from parachutes are often used in atmospheric profiling applications. They drift freely and often end up landing in inaccessible regions that make their retrieval challenging or impossible. In this paper, we develop and evaluate an approach using a multirotor unmanned aerial system to autonomously retrieve the parachute while it is still in the air. The system relies only on the initial conditions of the parachute-payload system and feedback from the vehicle's onboard cameras to track and then intercept the parachute mid-air in under 40 seconds on average. We present the results from our field experiments where we demonstrate the feasibility of the system and discuss its applicability to long-term payload transportation systems.