Author name cluster

Stephen J. Guy

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

2 author rows

ICRA Conference 2025 Conference Paper

Simultaneous Localization and Affordance Prediction of Tasks from Egocentric Video

Zachary Chavis
Hyun Soo Park
Stephen J. Guy

Vision-Language Models (VLMs) have shown great success as foundational models for downstream vision and natural language applications in a variety of domains. However, these models are limited to reasoning over objects and actions currently visible on the image plane. We present a spatial extension to the VLM, which leverages spatially-localized egocentric video demonstrations to augment VLMs in two ways - through understanding spatial task-affordances, i. e. where an agent must be for the task to physically take place, and the localization of that task relative to the egocentric viewer. We show our approach outperforms the baseline of using a VLM to map similarity of a task's description over a set of location-tagged images. Our approach has less error both on predicting where a task may take place and on predicting what tasks are likely to happen at the current location. The resulting representation will enable robots to use egocentric sensing to navigate to, or around, physical regions of interest for novel tasks specified in natural language.

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IJCAI Conference 2016 Conference Paper

Moving in a Crowd: Safe and Efficient Navigation among Heterogeneous Agents

Julio Godoy
Ioannis Karamouzas
Stephen J. Guy
Maria Gini

Multi-agent navigation methods typically assume that all agents use the same underlying framework to navigate to their goal while avoiding colliding with each other. However, such assumption does not hold when agents do not know how other agents will move. We address this issue by proposing a Bayesian inference approach where an agent estimates the navigation model and goal of each neighbor, and uses this to compute a plan that minimizes collisions while driving it to its goal. Simulation experiments performed in many scenarios demonstrate that an agent using our approach computes safer and more time-efficient paths as compared to those generated without our inference approach anda state-of-the-art local navigation framework.

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ICRA Conference 2015 Conference Paper

Prioritized group navigation with Formation Velocity Obstacles

Ioannis Karamouzas
Stephen J. Guy

We introduce the problem of navigating a group of robots having prioritized formations amidst static and dynamic obstacles. Our formulation allows users to define a number of template formations, each with a specified priority value. At each planning cycle, we compute a new formation which accounts for both these priority values and the safe progress of the robots towards their goal. To this end, we introduce a new velocity-based navigation approach which we denote as Formation Velocity Obstacles (FVO). Like other velocity-based approaches, FVO allows anticipatory collision avoidance accounting for the likely future motion of nearby obstacles. However, we extend these previous approaches and allow anisotropic agents which rotate themselves to orient along their direction of travel. We integrate these FVOs with a Bayesian framework to infer priority values for arbitrary formations from the user-given templates. The result is a complete framework for prioritized formation planning.

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ICRA Conference 2015 Conference Paper

Stochastic Tree Search with Useful Cycles for patrolling problems

Bilal Kartal
Julio Godoy
Ioannis Karamouzas
Stephen J. Guy

An autonomous robot team can be employed for continuous and strategic coverage of arbitrary environments for different missions. In this work, we propose an anytime approach for creating multi-robot patrolling policies. Our approach involves a novel extension of Monte Carlo Tree Search (MCTS) to allow robots to have life-long, cyclic policies so as to provide continual coverage of an environment. Our proposed method can generate near-optimal policies for a team of robots for small environments in real-time (and in larger environments in under a minute). By incorporating additional planning heuristics we are able to plan coordinated patrolling paths for teams of several robots in large environments quickly on commodity hardware.

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IROS Conference 2014 Conference Paper

Anytime navigation with Progressive Hindsight optimization

Julio Godoy
Ioannis Karamouzas
Stephen J. Guy
Maria L. Gini

In multi-robot systems, efficiently navigating in a a partially-known environment is an ubiquitous but challenging task, as each robot must account for the uncertainty introduced, for example, by other moving robots. This uncertainty makes pre-computed plans not always applicable, and often hinders the desired efficient use of the robot's resources. In this work, we present a local anytime approach for robot motion planning that accounts for the uncertainty of the environment by generating ‘snapshots’ of possible future scenarios. Our approach adapts the Hindsight optimization technique to allow robots to plan their immediate motion based on long-term efficiency. We validate our approach by comparing the efficiency on the paths executed against a state-of-the art navigation technique in a variety of scenarios, and show that by accounting for the uncertainty in the environment, agents can improve their time- and energy-efficient motions.

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ICRA Conference 2012 Conference Paper

LQG-obstacles: Feedback control with collision avoidance for mobile robots with motion and sensing uncertainty

Jur van den Berg
David Wilkie
Stephen J. Guy
Marc Niethammer
Dinesh Manocha

This paper presents LQG-Obstacles, a new concept that combines linear-quadratic feedback control of mobile robots with guaranteed avoidance of collisions with obstacles. Our approach generalizes the concept of Velocity Obstacles [3] to any robotic system with a linear Gaussian dynamics model. We integrate a Kalman filter for state estimation and an LQR feedback controller into a closed-loop dynamics model of which a higher-level control objective is the “control input”. We then define the LQG-Obstacle as the set of control objectives that result in a collision with high probability. Selecting a control objective outside the LQG-Obstacle then produces collision-free motion. We demonstrate the potential of LQG-Obstacles by safely and smoothly navigating a simulated quadrotor helicopter with complex non-linear dynamics and motion and sensing uncertainty through three-dimensional environments with obstacles and narrow passages.

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ICRA Conference 2011 Conference Paper

Reciprocal collision avoidance with acceleration-velocity obstacles

Jur van den Berg
Jamie Snape
Stephen J. Guy
Dinesh Manocha

We present an approach for collision avoidance for mobile robots that takes into account acceleration constraints. We discuss both the case of navigating a single robot among moving obstacles, and the case of multiple robots reciprocally avoiding collisions with each other while navigating a common workspace. Inspired by the concept of velocity obstacles [3], we introduce the acceleration-velocity obstacle (AVO) to let a robot avoid collisions with moving obstacles while obeying acceleration constraints. AVO characterizes the set of new velocities the robot can safely reach and adopt using proportional control of the acceleration. We extend this concept to reciprocal collision avoidance for multi-robot settings, by letting each robot take half of the responsibility of avoiding pairwise collisions. Our formulation guarantees collision-free navigation even as the robots act independently and simultaneously, without coordination. Our approach is designed for holonomic robots, but can also be applied to kinematically constrained non-holonomic robots such as cars. We have implemented our approach, and we show simulation results in challenging environments with large numbers of robots and obstacles.

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AAMAS Conference 2010 Conference Paper

Independent Navigation of Multiple Robots and Virtual Agents

Jamie Snape
Stephen J. Guy
Jur van den Berg
Sean Curtis
Sachin Patil
Ming C. Lin
Dinesh Manocha

We demonstrate an approach for collision- and oscillation-free navigation of multiple robots or virtual agents amongsteach other. Each entity acts independently and uses onlyboth the position and velocity of nearby entities to predicttheir future trajectories in order to avoid collisions. Entitiestake into account that the other entities are responding tothem likewise to prevent oscillations.

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AAMAS Conference 2010 Conference Paper

Modeling Collision Avoidance Behavior for Virtual Humans

Stephen J. Guy
Ming C. Lin
Dinesh Manocha

In this paper, we present a new trajectory planning algorithm for virtual humans. Our approach focuses on implicitcooperation between multiple virtual agents in order to sharethe work of avoiding collisions with each other. Specifically, we extend recent work on multi-robot planning to bettermodel how humans avoid collisions by introducing new parameters that model human traits, such as reaction timeand biomechanical limitations. We validate this new modelbased on data of real humans walking captured by the Locanthrope project. We also show how our model extendsto complex scenarios with multiple agents interacting witheach other and avoiding nearby obstacles.

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IROS Conference 2010 Conference Paper

Smooth and collision-free navigation for multiple robots under differential-drive constraints

Jamie Snape
Jur van den Berg
Stephen J. Guy
Dinesh Manocha

We present a method for smooth and collision-free navigation for multiple independent robots under differential-drive constraints. Our algorithm is based on the optimal reciprocal collision avoidance formulation and guarantees both smoothness in the trajectories of the robots and locally collision-free paths. We provide proofs of these guarantees and demonstrate the effectiveness of our method in experimental scenarios using iRobot Create mobile robots navigating amongst each other.

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IROS Conference 2009 Conference Paper

Independent navigation of multiple mobile robots with hybrid reciprocal velocity obstacles

Jamie Snape
Jur van den Berg
Stephen J. Guy
Dinesh Manocha

We present an approach for smooth and collision-free navigation of multiple mobile robots amongst each other. Each robot senses its surroundings and acts independently without central coordination or communication with other robots. Our approach uses both the current position and the velocity of other robots to predict their future trajectory in order to avoid collisions. Moreover, our approach is reciprocal and avoids oscillations by explicitly taking into account that the other robots also sense their surroundings and change their trajectories accordingly. We build on prior work related to velocity obstacles and reciprocal velocity obstacles and introduce the concept of hybrid reciprocal velocity obstacles for collision avoidance that takes into account the kinematics of the robots and uncertainty in sensor data. We apply our approach to a set of iRobot Create robots using centralized sensing and show natural, direct, and collision-free navigation in several challenging scenarios.

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