Christoforos Mavrogiannis

University of Michigan | U-M · Department of Robotics

We focus on robot navigation in crowded environments. The challenge of predicting the motion of a crowd around a robot makes it hard to ensure human safety and comfort. Recent approaches often employ end-to-end techniques for robot control or deep architectures for high-fidelity human motion prediction. While these methods achieve important perform...

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progres...

State-of-the-art social robot navigation algorithms often lack a thorough experimental validation in human environments: simulated evaluations are often conducted under unrealistically strong assumptions that prohibit deployment in real world environments; experimental demonstrations that are limited in sample size do not provide adequate evidence...

We focus on decentralized navigation among multiple non-communicating rational agents at \emph{uncontrolled} intersections, i.e., street intersections without traffic signs or signals. Avoiding collisions in such domains relies on the ability of agents to predict each others' intentions reliably, and react quickly. Multiagent trajectory prediction...

We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. While this prediction problem can be shown to be NP-hard, humans...

We focus on robot navigation in crowded environments. To navigate safely and efficiently within crowds, robots need models for crowd motion prediction. Building such models is hard due to the high dimensionality of multiagent domains and the challenge of collecting or simulating interaction-rich crowd-robot demonstrations. While there has been impo...

We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardware. Centralized replanning approaches become intrac...

Despite the structure of road environments, imposed via geometry and rules, traffic flows exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to the high dimensionality of possible behavior, the heterogeneity of agents, and the stochasticity of their decision-making. Modeling approaches learning associations in Euc...

We focus on robot navigation in crowded environments. To navigate safely and efficiently within crowds, robots need models for crowd motion prediction. Building such models is hard due to the high dimensionality of multiagent domains and the challenge of collecting or simulating interaction-rich crowd-robot demonstrations. While there has been impo...

We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardware. Centralized replanning approaches become intrac...

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progres...

We focus on decentralized navigation among multiple non-communicating agents at uncontrolled street intersections. Avoiding collisions under such settings demands nuanced implicit coordination. This is challenging to accomplish; the high dimensionality of the space of possible behavior and the lack of explicit communication among agents complicate...

We focus on robot navigation in crowded environments. The challenge of predicting the motion of a crowd around a robot makes it hard to ensure human safety and comfort. Recent approaches often employ end-to-end techniques for robot control or deep architectures for high-fidelity human motion prediction. While these methods achieve important perform...

During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such meas...

Mobile robots struggle to integrate seamlessly in crowded environments such as pedestrian scenes, often disrupting human activity. One obstacle preventing their smooth integration is our limited understanding of how humans may perceive and react to robot motion. Motivated by recent studies highlighting the benefits of intent-expressive motion for r...

We focus on decentralized navigation among multiple non-communicating agents at uncontrolled street intersections. Avoiding collisions under such settings demands nuanced implicit coordination. This is challenging to accomplish; the high dimensionality of the space of possible behavior and the lack of explicit communication among agents complicate...

We focus on the problem of analyzing multiagent interactions in traffic domains. Understanding the space of behavior of real-world traffic may offer significant advantages for algorithmic design, data-driven methodologies, and bench-marking. However, the high dimensionality of the space and the stochasticity of human behavior may hinder the identif...

During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such meas...

Highly articulated organisms serve as blueprints for incredibly dexterous mechanisms, but building similarly capable robotic counterparts has been hindered by the difficulties of developing electromechanical actuators with both the high strength and compactness of biological muscle. We develop a stackable electrostatic brake that has comparable spe...

We focus on the problem of analyzing multiagent interactions in traffic domains. Understanding the space of behavior of real-world traffic may offer significant advantages for algorithmic design, data-driven methodologies, and benchmarking. However, the high dimensionality of the space and the stochasticity of human behavior may hinder the identifi...

While prior work has shown how to autonomously generate motion that communicates task-related attributes, like intent or capability, we know less about how to automatically generate motion that communicates higher-level be-havioral attributes such as curiosity or competence. We propose a framework that addresses the challenges of modeling human att...

We focus on the problem of planning the motion of a robot in a dynamic multiagent environment such as a pedestrian scene. Enabling the robot to navigate safely and in a socially compliant fashion in such scenes requires a representation that accounts for the unfolding multiagent dynamics. Existing approaches to this problem tend to employ microscop...

Robots deployed in human-populated spaces often need human help to effectively complete their tasks. Yet, a robot that asks for help too frequently or at the wrong times may cause annoyance, and a robot that asks too infrequently may be unable to complete its tasks. In this paper, we present a model of humans' helpfulness towards a robot in an offi...

We focus on decentralized navigation among multiple non-communicating rational agents at uncontrolled intersections, i.e., street intersections without traffic signs or signals. Avoiding collisions in such domains relies on the ability of agents to predict each others' intentions reliably, and react quickly. Multiagent trajectory prediction is NP-h...

Chopsticks constitute a simple yet versatile tool that humans have used for thousands of years to perform a variety of challenging tasks ranging from food manipulation to surgery. Applying such a simple tool in a diverse repertoire of scenarios requires significant adaptability. Towards developing autonomous manipulators with comparable adaptabilit...

We focus on navigation among rational, non-communicating agents at unsignalized street intersections. Following collision-free motion under such settings demands nuanced \emph{implicit} coordination among agents. Often, the structure of these domains constrains multiagent trajectories to belong to a finite set of modes. Our key insight is that empo...

We present a planning framework for decentralized navigation in dynamic multi-agent environments where no explicit communication takes place among agents. Our framework is based on a novel technique for computationally efficient multi-agent trajectory generation from symbolic topological specifications. At planning time, this technique allows an ag...

We present a novel planning framework for navigation in dynamic, multi-agent environments with no explicit communication among agents, such as pedestrian scenes. Inspired by the collaborative nature of human navigation, our approach treats the problem as a coordination game, in which players coordinate to avoid each other as they move towards their...

We focus on the problem of designing an artificial agent (AI), capable of assisting a human user to complete a task. Our goal is to guide human users towards optimal task performance while keeping their cognitive load as low as possible. Our insight is that doing so requires an understanding of human decision making for the task domain at hand. In...

We focus on the problem of designing an artificial agent (AI), capable of assisting a human user to complete a task. Our goal is to guide human users towards optimal task performance while keeping their cognitive load as low as possible. Our insight is that doing so requires an understanding of human decision making for the task domain at hand. In...

We present MuSHR, the Multi-agent System for non-Holonomic Racing. MuSHR is a low-cost, open-source robotic racecar platform for education and research, developed by the Personal Robotics Lab in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. MuSHR aspires to contribute towards democratizing the field of...

Crowded human environments such as pedestrian scenes constitute challenging domains for mobile robot navigation, for a variety of reasons including the heterogeneity of pedestrians’ decision-making mechanisms, the lack of channels of explicit communication among them and the lack of universal rules or social conventions regulating traffic. Despite...

We present a navigation planning framework for dynamic, multi-agent environments, where no explicit communication takes place among agents. Inspired by the collaborative nature of human navigation, our approach encodes the concept of coordination into an agent's decision making through an inference mechanism about collaborative strategies of collis...

We present a planning framework for decentralized navigation in dynamic multi-agent environments where no explicit communication takes place among agents. Our framework is based on a novel technique for computationally efficient multi-agent trajectory generation from symbolic topological specifications. At planning time, this technique allows an ag...

Intent-expressive robot motion has been shown to result in increased efficiency and reduced planning efforts for copresent humans. Existing frameworks for generating intent-expressive robot behaviors have typically focused on applications in static or structured environments. Under such settings, emphasis is placed towards communicating the robot's...

We present a novel, data-driven framework for planning socially competent robot behaviors in crowded environments. The core of our approach is a topological model of collective navigation behaviors, based on braid groups. This model constitutes the basis for the design of a human-inspired probabilistic inference mechanism that predicts the topology...

We present a framework for online navigation planning in multi-agent environments, where no explicit communication takes place among agents, such as pedestrian scenes. Inspired by pedestrian navigation, our approach encodes the concept of coordination into agents' decision making through an inference mechanism about joint strategies of avoidance. S...

Despite the great progress in robotic navigation in the past decades, navigating a human environment remains a hard task for a robot, due to the lack of formal rules guiding traffic, the lack of explicit communication among agents and the unpredictability of human behavior. Inspired by the efficiency of human navigation, we employ the insights of s...

Robots must be cognizant of how their actions will be interpreted in context. Actions performed in the context of a joint activity comprise two aspects: functional and communicative. The functional component achieves the goal of the action, whereas its communicative component, when present, expresses some information to the actor's partners in the...

We present a novel planning framework for navigation in dynamic, multi-agent environments with no explicit communication among agents, such as pedestrian scenes. Inspired by the collaborative nature of human navigation, our approach treats the problem as a coordination game, in which players coordinate to avoid each other as they move towards their...

We present a planning framework for producing socially competent robot behaviors in pedestrian environments. The framework is designed according to conclusions of recent psychology studies on action interpretation and sociology studies on human pedestrian behavior. The core of the approach is a novel topological representation of the pedestrian sce...

We present a novel framework for socially competent pedestrian navigation based on understanding pedestrians’ intentions and planning intent-expressive robot motion. We model pedestrians’ intentions as combinations of intended topological routes and intended destinations. The core of this approach is a novel topological representation of a pedestri...

This technical report is intended to serve as a step by step tutorial for the replication of a prosthetic and developed by the OpenBionics (www.openbionics.org) initiative. Based on a previous design for the OpenBionics robotic hands, the prosthetic hand was developed to be affordable, light-weight and intrinsically-compliant. The proposed design i...

In this paper we present an open-source design for the development of low-complexity, anthropomorphic, un-deractuated robot hands with a selectively lockable differential mechanism. The differential mechanism used is a variation of the whiffletree (or seesaw) mechanism, which introduces a set of locking buttons that can block the motion of each fin...

In this paper we introduce an index for the quantification of anthropomorphism of robot arms. The index is defined as a weighted sum of specific metrics which evaluate the similarities between the human and robot arm workspaces, providing a normalized score between 0 (non-anthropomorphic artifacts) and 1 (human-identical artifacts). The human arm w...

OpenBionics is an open-source initiative for the development of affordable, lightweight , modular, underactuated robot hands and myoelectric prosthetic devices, that can be easily reproduced using off-the-shelf materials. The primary focus of the presented initiative is to propose designs that facilitate the creation of multiple low-cost task-speci...

In this paper we present a series of design direc-tions for the development of affordable, modular, light-weight, intrinsically-compliant, underactuated robot hands, that can be easily reproduced using off-the-shelf materials. The proposed robot hands, efficiently grasp a series of everyday life objects and are considered to be general purpose, as...

In this paper, we propose an optimization scheme for deriving task-specific force closure grasps for underactuated robot hands. Motivated by recent neuroscientific studies on the human grasping behavior, a novel grasp strategy is built upon past analysis regarding the task-specificity of human grasps, that also complies with the recent soft synergy...

In this work, we present a novel concept in the area of optimal grasp synthesis, confronting both geometric and mechanical constraints. Initializing from a locally optimal force distribution on some predefined feasible contact points, our method improves gradually the grasp quality avoiding simultaneously singularities and mechanical limitations. T...

The development of complex, human-like, multi-fingered robot hands, aiming at being incorporated in household robotics, prosthetics or even in industrial applications and space has brought the problem of grasping in the spotlight of modern robotics research. Grasping is a multiparametric problem during which the mechanical system (robot hand) inter...