Asynchronous and Parallel Distributed Pose Graph Optimization

A recent paper by members of the DCIST alliance has received a 2020 honorable mention from IEEE Robotics and Automation Letters. The paper presents Asynchronous Stochastic Parallel Pose Graph Optimization (ASAPP), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates […]

Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams

A recent paper by members of the DCIST alliance considers the problem of planning trajectories for a team of sensor-equipped robots to reduce uncertainty about a dynamical process. Optimizing the trade-off between information gain and energy cost (e.g., control effort, energy expenditure, distance travelled) is desirable but leads to a non-monotone objective function in the […]

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

A recent paper by members of the DCIST alliance develops an open-source C++ library for real-time metric- semantic visual-inertial Simultaneous Localization And Mapping (SLAM). The library goes beyond existing visual and visual-inertial SLAM libraries (e.g., ORB-SLAM, VINSMono, OKVIS, ROVIO) by enabling mesh reconstruction and semantic labeling in 3D. Kimera is designed with modularity in mind […]

Asymptotically Optimal Planning for Non-myopic Multi-Robot Information Gathering

A recent paper by members of the DCIST alliance develops a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active Simultaneous Localization and Mapping (SLAM), surveillance, environmental monitoring and others. The goal is to compute control policies for mobile robot […]

Active Exploration in Signed Distance Fields

When performing tasks in unknown environments it is useful for a team of robots to have a good map of the area to assist in efficient, collision-free planning and navigation. A recent paper by members of the DCIST alliance tackles the problem of autonomous mapping of unknown environments using information theoretic metrics and signed distance […]

Learning Multi-Agent Policies from Observations

A recent paper from the DCIST team introduces a framework for learning to perform multi-robot missions by observing an expert system executing the same mission. The expert system is a team of robots equipped with a library of controllers, each designed to solve a specific task. The expert system’s policy selects the controller necessary to […]

Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors

A recent paper by members of the DCIST alliance develops the use of reinforcement learning techniques to train policies in simulation that transfer remarkably well to multiple different physical quadrotors. Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. The policies developed are low-level, i.e., they map the rotorcrafts’ state directly to the […]

Planning with Uncertain Specifications (PUnS)

Consider the task of setting a dinner table. It involves placing the appropriate serving utensils and silverware according to the dishes being served. Some of the objects need to be placed in a particular order as they might be stacked on top of each other or due to cultural traditions. Many real-world tasks demonstrate such […]

Synthesis of a Time-Varying Communication Network by Robot Teams with Information Propagation Guarantees

A recent paper by Xi Yu and M. Ani Hsieh from the University of Pennsylvania presents a distributed control and coordination strategy that allows a swarm of mobile robots to form an intermittently connected communication network as they monitor a given environment. The approach assumes robots are tasked to patrol a set of perimeters in […]

Learning Decentralized Controllers with Graph Neural Networks

A recent paper by members of the DCIST alliance develops a method for distributed control of large networks of mobile robots with interacting dynamics and sparsely available communications. Their approach is to learn local controllers that require only local information and communications at test time by imitating the policy of centralized controllers using global information […]

Heterogeneity and Uncertainty in Perimeter Defense

Surveillance of perimeters and securing perimeters are important tasks in civilian and military defense applications, and it has become practical to deploy a large number of autonomous agents to address these problems using multi-robot systems.   A recent paper by members of the DCIST alliance formulates this scenario as a variant of multi-player pursuit-evasion games, where […]

Human Information Processing in Complex Networks

In this work, we study the structure of real-world communication systems to understand how information can be rapidly and efficiently communicated to humans, for example from swarms of drones or other agents. Humans constantly receive information from systems of interconnected stimuli or concepts — from language and music to literature and science — yet it […]

Resilient Active Information Acquisition with Mobile Robots

In the future, teams of heterogeneous robot teams will be operating in unknown and adversarial environments.   In failure prone or adversarial environments, the capability of resilience is crucial to ensuring the robots can complete their mission. Mission resilience to robot failures, sensor attacks or communication disruptions is currently an afterthought leading to optimal over-provisioned designs.   […]

Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation

While many distributed reinforcement learning (RL) has emerged as one of the important paradigms in distributed control, we are only beginning to understand the fundamental behavior of these algorithms.  Two recent papers from the DCIST alliance provide important progress in this direction. In the multi-agent policy evaluation problem, a group of agents operate in a […]

Learning to Learn with Probabilistic Task Embeddings

To operate successfully in a complex and changing environment, learning agents must be able to acquire new skills quickly. Humans display remarkable skill in this area — we can learn to recognize a new object from one example, adapt to driving a different car in a matter of minutes, and add a new slang word […]

Localization and Mapping using Instance-specific Mesh Models

A recent paper by members of the DCIST alliance proposes an approach for building semantic maps, containing object poses and shapes, in real time, onboard an autonomous robot equippend with a monocular camera. Rich understanding of the geometry and context of a robot’s surroundigs is important for specification and safe, efficient execution of complex missions. This […]

Aerial Robot Prototype

CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-Agent Data Association

Composable Autonomy in Heterogeneous Groups

In multi-robot Simultaneous Localization and Mapping (SLAM), a group of robots explore and map an unknown area. The group can benefit from its size by combining the robots’ maps to improve coverage and by each robot using shared information to improve its own localization. Most approaches to multi-robot SLAM consider homogeneous groups, in which all […]