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Thomas M. Howard

14 accepted papers

2025

Far-Field Image-Based Traversability Mapping for a Priori Unknown Natural Environments

Ethan Fahnestock, Erick Fuentes, Samuel Prentice, Vasileios Vasilopoulos, Philip R. Osteen, Thomas M. Howard +1
RA-L 2025

While navigating unknown environments, robots rely primarily on proximate features for guidance in decision making, such as depth information from lidar to build a costmap, or local semantic information from images. The limited range over which these features can be used may result in poor robot beh

Cited by 2Source ↗code ↗Scholar ↗
2025

Homotopy-Aware Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Cluttered Environments

Ashwin Menon, Eric R. Damm, Thomas M. Howard
RA-L 2025

Mobile robot navigation architectures that employ a planning algorithm to provide a single optimal path to follow are flawed in the presence of unstructured, rapidly changing environments. As the environment updates, optimal plans often oscillate around discrete obstacles, which is problematic for p

Cited by 4Source ↗Scholar ↗
2023

Language Guided Temporally Adaptive Perception for Efficient Natural Language Grounding in Cluttered Dynamic Worlds

Siddharth Patki, Jacob Arkin, Nikola Raicevic, Thomas M. Howard
IROS 2023poster

As robots operate alongside humans in shared spaces, such as homes and offices, it is essential to have an effective mechanism for interacting with them. Natural language offers an intuitive interface for communicating with robots, but most of the recent approaches to grounded language understanding…

Cited by 3Source ↗Scholar ↗
2023

Terrain-Aware Kinodynamic Planning with Efficiently Adaptive State Lattices for Mobile Robot Navigation in Off-Road Environments

Eric R. Damm, Jason M. Gregory, Eli S. Lancaster, Felix A. Sanchez, Daniel M. Sahu, Thomas M. Howard
IROS 2023

To safely traverse non-flat terrain, robots must account for the influence of terrain shape in their planned motions. Terrain-aware motion planners use an estimate of the vehicle roll and pitch as a function of pose, vehicle suspension, and ground elevation map to weigh the cost of edges in the sear

Cited by 11Source ↗Scholar ↗
2022

Improved Performance of CPG Parameter Inference for Path-following Control of Legged Robots

Nathan D. Kent, David Neiman, Matthew Travers, Thomas M. Howard
IROS 2022poster

The difficulty associated with the coordinated locomotion of legged robots grows quickly as the number of joints increases. Although prior approaches have addressed this problem through sampling-based planners, learning-based techniques have recently been explored as a means to handle such complexit…

Cited by 3Source ↗Scholar ↗
2021

Discrete Optimization of Adaptive State Lattices for Iterative Motion Planning on Unmanned Ground Vehicles

Benned Hedegaard, Ethan Fahnestock, Jacob Arkin, Ashwin Menon, Thomas M. Howard
IROS 2021poster

Robust motion planners for unmanned ground vehicles must minimize risk while obeying vehicle mobility constraints. Algorithms such as the State Lattice (SL) utilize offline computation to generate expressive control sets which form recombinant search spaces, enabling the use of heuristic search to e…

Cited by 12Source ↗Scholar ↗
2020

Inferring Task-Space Central Pattern Generator Parameters for Closed-loop Control of Underactuated Robots

Nathan D. Kent, Raunaq M. Bhirangi, Matthew Travers, Thomas M. Howard
ICRA 2020poster

The complexity associated with the control of highly-articulated legged robots scales quickly as the number of joints increases. Traditional approaches to the control of these robots are often impractical for many real-time applications. This work thus presents a novel sampling-based planning approa…

Cited by 2Source ↗Scholar ↗
2019

Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions

Siddharth Patki, Andrea F. Daniele, Matthew R. Walter, Thomas M. Howard
ICRA 2019poster

The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been paid to developing models and approximate inference algorithms that improve the efficiency of language understanding. Howe…

Cited by 27Source ↗Scholar ↗
2019

Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments

Siddharth Patki, Ethan Fahnestock, Thomas M. Howard, Matthew R. Walter
CoRL 2019

Recent advances in data-driven models for grounded language understanding have enabled robots to interpret increasingly complex instructions. Two fundamental limitations of these methods are that most require a full model of the environment to be known a priori, and they attempt to reason over a wor

Cited by 0Source ↗PDF ↗Scholar ↗
2017

On the performance of selective adaptation in state lattices for mobile robot motion planning in cluttered environments

Michael E. Napoli, Harel Biggie, Thomas M. Howard
IROS 2017poster

Autonomous mobile robots require motion planning algorithms that match limitations of on-board computing resources to safely navigate complex environments. In situations where near-optimality is preferential to runtime performance, search spaces that optimize their local connectivity to improve the…

Cited by 3Source ↗Scholar ↗
2016

Efficient Grounding of Abstract Spatial Concepts for Natural Language Interaction with Robot Manipulators

Rohan Paul, Jacob Arkin, Nicholas Roy, Thomas M. Howard
RSS 2016poster

Our goal is to develop models that allow a robot to understand natural language instructions in the context of its world representation. Contemporary models learn possible correspondences between parsed instructions and candidate groundings that include objects, regions and motion constraints. Howev…

Cited by 127Source ↗PDF ↗Scholar ↗
2016

Expressing homotopic requirements for mobile robot navigation through natural language instructions

Daqing Yi, Thomas M. Howard, Michael A. Goodrich, Kevin D. Seppi
IROS 2016poster

Allowing a human to express topological requirements to a robot in language enables untrained users to guide robot movement without requiring the human to understand sophisticated robot algorithms. By using a homotopy class or classes to represent one or more topological requirements, we build a fra…

Cited by 10Source ↗Scholar ↗
2015

Learning models for following natural language directions in unknown environments

Sachithra Hemachandra, Felix Duvallet, Thomas M. Howard, Nicholas Roy, Anthony Stentz, Matthew R. Walter
ICRA 2015poster

Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret natural language manipulation and navigation commands, but…

Cited by 100Source ↗Scholar ↗
2015

On the performance of hierarchical distributed correspondence graphs for efficient symbol grounding of robot instructions

Istvan Chung, Oron Propp, Matthew R. Walter, Thomas M. Howard
IROS 2015poster

Natural language interfaces are powerful tools that enables humans and robots to convey information without the need for extensive training or complex graphical interfaces. Statistical techniques that employ probabilistic graphical models have proven effective at interpreting symbols that represent…

Cited by 45Source ↗Scholar ↗