PhD Candidate · WPI · Columbia · Boston, MA

Steven
Hyland.

I build robots that reason about the physical world — specializing in manipulation, dynamics modeling, and bridging simulation to reality. Currently finishing a PhD in Robotics Engineering at WPI.

Steven Hyland

Physics-first.
Learning-forward.

I'm a PhD candidate at Worcester Polytechnic Institute, where I research active perception and parameter estimation for robot manipulation. My work focuses on letting robots figure out the physical properties of objects they've never encountered before — without being told in advance.

My background is rooted in analytical, model-based methods — robot dynamics, sim2real transfer, and model-based control. I'm increasingly combining these with modern machine learning to build systems that are both physically grounded and data-driven.

Before my PhD, I deployed and programmed autonomous mobile robots for Fortune 500 clients at Seegrid. I hold a B.S. in Mechanical Engineering from Columbia University. I'm looking for research engineer roles where physical grounding matters — where simulation meets hardware and systems ship.

Robot Dynamics Manipulation Sim2Real Active Perception MuJoCo ROS 1 & 2 Python / PyTorch C++ MATLAB
Presidential Fellow
Worcester Polytechnic Institute
NRT FORW-RD Fellow
National Science Foundation (NSF)
Glenn Yee Award Recipient
Worcester Polytechnic Institute
IEEE RAS President
Worcester Polytechnic Institute
Kings Crown Leadership Award
Columbia University
Research

Current work.

IEEE IROS 2026 · Under Review
Before the Tipping Point: Force-Guided Active Perception for Shape-Agnostic Estimation of 3D Centers of Mass
A robot manipulator estimates CoM height and mass from a single sub-critical tipping interaction — no object geometry required. Using quasistatic push–retract cycles and force-angle measurements, the method recovers inertial parameters with under 5% relative error across objects of varying shape, mass, and contact properties.
IEEE CASE 2025
CoM Estimation with Onboard Sensing & Pushing for Mobile Robots
 
A framework for estimating the 3D center of mass of unknown payloads using a holonomic mobile robot. Combines active pushing, onboard sensing, and model-based estimation — no prior knowledge of the object required.
New England Manipulation Symposium 2025
Onboard Sensing & Pushing of Unknown Payloads for CoM Estimation with a Holonomic Mobile Robot
NEMS 2025 presentation
Oral presentation.
IEEE IROS 2023
Predicting Center of Mass by Iterative Pushing
 
Iterative pushing strategy to infer object CoM for transportation and manipulation tasks. Validated on physical hardware with a robot arm, demonstrating robust estimation across payload geometries.
Projects

Built over time.

2026
Online Parameter Estimation with RL (In Progress)
MuJoCo RL Python PyTorch

Simulation environment in MuJoCo where a robot arm must estimate the mass and inertia of unknown objects during a manipulation task — using reinforcement learning rather than analytical estimators. Extension of PhD work into learned dynamics.

2023
OddRugs — Optimal Dynamic Distribution of Robots via Graph Search
Python Multi-Robot Graph Search

Multi-robot task allocation system using graph-based search to dynamically distribute robots across a manipulated payload. Optimizes coverage and throughput under real-time constraints.

2023
Constraint-Based 3D Haptic Interaction Simulation
C++ Haptics Simulation

Implemented a constraint-based haptic simulation framework for 3D interaction. Models contact forces and physical constraints to produce realistic tactile feedback in a virtual environment. Low-level OpenGL implementation with real-time performance.

2022
Deep RL for Robotic Pick-and-Place — DQN with Franka Arm
Deep RL DQN C++ Simulation

Applied Deep Q-Network (DQN) to teach a simulated Franka robotic arm to pick objects in a structured environment. Trained entirely in simulation with a learned value function driving grasp selection — course final project for Deep RL (595).

2019
Tensegrity Tumbleweed Rover — Columbia Capstone
Mechanical Design SolidWorks Prototyping

Senior capstone project at Columbia: designed and built a tensegrity-based rolling robot for planetary exploration. The structure uses tensile integrity to survive impacts without rigid housing.

Experience

Where I've worked.

Aug 2021
— Present
Doctoral Research Assistant
Worcester Polytechnic Institute
  • Developed 3D CoM estimation framework combining active perception, non-prehensile pushing, and model-based control for arbitrary payloads — published at IROS 2023 and CASE 2025.
  • Built high-fidelity simulation pipeline in MuJoCo; extending with RL-based learned dynamics for generalization.
  • Designed graph-based optimal grasp configuration planner using modified BFS.
  • Mentored undergraduate researchers; led outreach for NSF FORW-RD and IEEE RAS.
Nov 2019
— Aug 2021
Implementation Engineer
Seegrid · Pittsburgh, PA
  • Programmed and deployed AMRs for Fortune 500 clients including Amazon, GM, and UPS.
  • Designed fleet pathing logic for 20+ robot installations to maximize throughput.
  • Built Python tooling that cut per-site deployment time by ~20 hours.
Sep 2019
— Nov 2019
Robotic Design Engineer
Avar Robotics · New York, NY
  • Modeled next-generation inventory-sorting robot in SolidWorks with FEA validation.
  • Prototyped tetherless mobile redesign enabling infrastructure-free operation.
Skills

What I work with.

Robotics
  • Robot Dynamics Modeling
  • Sim2Real Transfer
  • Manipulation Planning
  • Parameter Estimation
  • Model-Based Control
  • Active Perception
  • Motion Planning
  • Multi-Robot Systems
Software & Tools
  • Python (NumPy, SciPy, PyTorch, OpenCV)
  • MuJoCo
  • ROS 1 & 2
  • C++
  • MoveIt
  • Gazebo
  • MATLAB
  • Git / Linux
ML / Learning
  • Reinforcement Learning
  • Learned Dynamics Models
  • Teleoperation
  • Gesture Recognition & Control
  • Optimization-Based Estimation
Contact

Get in touch.

I'm actively looking for research engineer and applied scientist roles in robotics and robot learning, based in Boston. Open to relocation for the right opportunity.