Control of Many Agents Using Few Instructions

Abstract — This paper considers the problem of controlling a group of agents under the constraint that every agent must be given the same control input. This problem is relevant for the control of mobile micro-robots that all receive the same power and control signals through an underlying substrate. Despite this restriction, several examples in simulation demonstrate that it is possible to get a group of micro-robots to perform useful tasks. All of these tasks are derived by thinking about the relationships between robots, rather than about their individual states. I

The Use of Multi-Scale Fiducial Markers To Aid Takeoff and Landing Navigation by Rotorcraft

This paper quantifies the impact of adverse environmental conditions on the detection of fiducial markers (i.e., artificial landmarks) by color cameras mounted on rotorcraft. We restrict our attention to square markers with a black-and-white pattern of grid cells that can be nested to allow detection at multiple scales. These markers have the potential to enhance the reliability of precision takeoff and landing at vertiports by flying vehicles in urban settings. Prior work has shown, in particular, that these markers can be detected with high precision (i.e., few false positives) and high recall (i.e., few false negatives). However, most of this prior work has been based on image sequences collected indoors with hand-held cameras. Our work is based on image sequences collected outdoors with cameras mounted on a quadrotor during semi-autonomous takeoff and landing operations under adverse environmental conditions that include variations in temperature, illumination, wind speed, humidity, visibility, and precipitation. In addition to precision and recall, performance measures include continuity, availability, robustness, resiliency, and coverage volume. We release both our dataset and the code we used for analysis to the public as open source.Comment: Extended abstract accepted at the 2024 AIAA SciTec

Comparative Study of Visual SLAM-Based Mobile Robot Localization Using Fiducial Markers

This paper presents a comparative study of three modes for mobile robot localization based on visual SLAM using fiducial markers (i.e., square-shaped artificial landmarks with a black-and-white grid pattern): SLAM, SLAM with a prior map, and localization with a prior map. The reason for comparing the SLAM-based approaches leveraging fiducial markers is because previous work has shown their superior performance over feature-only methods, with less computational burden compared to methods that use both feature and marker detection without compromising the localization performance. The evaluation is conducted using indoor image sequences captured with a hand-held camera containing multiple fiducial markers in the environment. The performance metrics include absolute trajectory error and runtime for the optimization process per frame. In particular, for the last two modes (SLAM and localization with a prior map), we evaluate their performances by perturbing the quality of prior map to study the extent to which each mode is tolerant to such perturbations. Hardware experiments show consistent trajectory error levels across the three modes, with the localization mode exhibiting the shortest runtime among them. Yet, with map perturbations, SLAM with a prior map maintains performance, while localization mode degrades in both aspects.Comment: IEEE 2023 IROS Workshop "Closing the Loop on Localization". For more information, see https://oravus.github.io/vpr-workshop/inde

Learning from Integral Losses in Physics Informed Neural Networks

This work proposes a solution for the problem of training physics informed networks under partial integro-differential equations. These equations require infinite or a large number of neural evaluations to construct a single residual for training. As a result, accurate evaluation may be impractical, and we show that naive approximations at replacing these integrals with unbiased estimates lead to biased loss functions and solutions. To overcome this bias, we investigate three types of solutions: the deterministic sampling approach, the double-sampling trick, and the delayed target method. We consider three classes of PDEs for benchmarking; one defining a Poisson problem with singular charges and weak solutions, another involving weak solutions on electro-magnetic fields and a Maxwell equation, and a third one defining a Smoluchowski coagulation problem. Our numerical results confirm the existence of the aforementioned bias in practice, and also show that our proposed delayed target approach can lead to accurate solutions with comparable quality to ones estimated with a large number of samples. Our implementation is open-source and available at https://github.com/ehsansaleh/btspinn

Hypergraph-based Multi-Robot Task and Motion Planning

We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, produces solution times up to three orders of magnitude faster than existing methods. We achieve this improvement by decomposing the planning space into subspaces for independent manipulators, objects, and manipulators holding objects. We represent this decomposition with a hypergraph where vertices are substates and hyperarcs are transitions between substates. Existing methods use graph-based representations where vertices are full states and edges are transitions between states. Using the hypergraph reduces the size of the planning space-for multi-manipulator object rearrangement, the number of hypergraph vertices scales linearly with the number of either robots or objects, while the number of hyperarcs scales quadratically with the number of robots and linearly with the number of objects. In contrast, the number of vertices and edges in graph-based representations scale exponentially in the number of robots and objects. Additionally, the hypergraph provides a structure to reason over varying levels of (de)coupled spaces and transitions between them enabling a hybrid search of the planning space. We show that similar gains can be achieved for other multi-robot task and motion planning problems.Comment: This work has been submitted for revie

Self-supervised 6D Object Pose Estimation for Robot Manipulation

To teach robots skills, it is crucial to obtain data with supervision. Since annotating real world data is time-consuming and expensive, enabling robots to learn in a self-supervised way is important. In this work, we introduce a robot system for self-supervised 6D object pose estimation. Starting from modules trained in simulation, our system is able to label real world images with accurate 6D object poses for self-supervised learning. In addition, the robot interacts with objects in the environment to change the object configuration by grasping or pushing objects. In this way, our system is able to continuously collect data and improve its pose estimation modules. We show that the self-supervised learning improves object segmentation and 6D pose estimation performance, and consequently enables the system to grasp objects more reliably. A video showing the experiments can be found at https://youtu.be/W1Y0Mmh1Gd8.Comment: Accepted to International Conference on Robotics and Automation (ICRA), 202

Querying the user properly for high-performance brain-machine interfaces: Recursive estimation, control, and feedback information-theoretic perspectives

We propose a complementary approach to the design of neural prosthetic interfaces that goes beyond the standard approach of estimating desired control signals from neural activity. We exploit the fact that the for a user’s intended application, the dynamics of the prosthetic in fact impact subsequent desired control inputs. We illustrate that changing the dynamic re-sponse of a prosthetic device can make specific tasks signif-icantly easier to accomplish. Our approach relies upon prin-ciples from stochastic control and feedback information the-ory, and we illustrate its effectiveness both theoretically and experimentally- in terms of spelling words from a menu of characters using binary surface electromyography classifica-tion. Index Terms — neural prosthetics, feedback information theory, stochastic control, interface design 1

Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface

Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain–computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain–computer interface and elicitation of an event-related potential (P300 wave)

Motion Planning of Multi-Limbed Robots Subject to Equilibrium Constraints: The Free-Climbing Robot Problem

This paper addresses the problem of planning the motion of a multi-limbed robot in order to “free-climb ” vertical rock surfaces. Free-climbing only relies on frictional contact with the surfaces rather than on special fixtures or tools like pitons. It requires strength, but more importantly it requires deliberate reasoning: not only must the robot decide how to adjust its posture to reach the next feature without falling, it must plan an entire sequence of steps, where each one might have future consequences. In this paper, this process of reasoning is broken into manageable pieces by decomposing a free-climbing robot’s configuration space into manifolds associated with each state of contact between the robot and its environment. A multi-step planning framework is presented that decides which manifolds to explore by generating a candidate sequence of hand and foot placements first. A one-step planning algorithm is then described that explores individual manifolds quickly. This algorithm extends the probabilistic roadmap approach to better handle the interaction between static equilibrium and the topology of closed kinematic chains. It is assumed throughout this paper that a set of potential contact points has been presurveyed. Validation with real hardware was done with a four-limbed robot called lemur (developed by the Mechanical and Robotic Technologies Group at nasa-jpl). Using the planner presented in this paper, lemur free-climbed an indoor, near-vertical surface covered with artificial rock features.