About

Seth Hutchinson is a professor and holds the KUKA Chair for Robotics at the College of Computing. He is also the Executive Director of the Institute for Robotics and Intelligent Machines (IRIM) at Georgia Tech. His research areas include robotics, and he is affiliated with the School of Interactive Computing and the Institute for Robotics and Intelligent Machines. His work focuses on advancing the field of robotics through innovative research and leadership, contributing to the development of intelligent robotic systems.

Research topics

• Computer Science
• Artificial Intelligence
• World Wide Web

Selected publications

• Modeling and Optimizing the Provisioning of Exhaustible Capabilities for Simultaneous Task Allocation and Scheduling

  2026-05-24

  articleOpen accessSenior author

  Deploying heterogeneous robot teams to accomplish multiple tasks over extended time horizons presents significant computational challenges for task allocation and planning. In this paper, we present a comprehensive, time-extended, offline heterogeneous multi-robot task allocation framework, TRAITS, which we believe to be the first that can cope with the provisioning of exhaustible traits under battery and temporal constraints. Specifically, we introduce a nonlinear programming-based trait distribution module that can optimize the trait-provisioning rate of coalitions to yield feasible and time-efficient solutions. TRAITS provides a more accurate feasibility assessment and estimation of task execution times and makespan by leveraging trait-provisioning rates while optimizing battery consumption---an advantage that state-of-the-art frameworks lack. We evaluate TRAITS against two state-of-the-art frameworks, with results demonstrating its advantage in satisfying complex trait and battery requirements while remaining computationally tractable.

  PublisherOA PDFDOI

• EmoBipedNav: Emotion-Aware Social Navigation for Bipedal Robots With Deep Reinforcement Learning

  IEEE/ASME Transactions on Mechatronics · 2026-01-01

  article

  This study presents an emotion-aware navigation framework—EmoBipedNav—using deep reinforcement learning (DRL) for bipedal robots walking in socially interactive environments. The inherent complex dynamics of bipedal robots challenge their safe maneuvering capabilities in dynamic environments. Furthermore, the intricacies of social interactions and cues such as emotions significantly compound the challenges of bipedal robot navigation. To address these coupled issues, we propose a two-stage pipeline that jointly considers the bipedal full-body dynamics and the complexities of socially aware navigation. More specifically, an emotion-integrated navigation policy is developed to balance safety, efficiency, and social courtesy by responding to human emotional cues. One key component of the policy is a novel representation of social environments using sequential LiDAR grid maps, from which we extract latent features, implicitly including collision regions, discomfort zones determined by emotions, social interactions, and the evolving dynamics of the navigation system including robot movements and pedestrian motions. To accounts for path tracking errors and locomotion constraints during social navigation, we present an end-to-end navigation system that incorporates full-order robot dynamics during training. Finally, extensive benchmarking and sim-to-real experiments demonstrate that our method outperforms both model-based planners and DRL baselines, and generalizes effectively to real-world environments.

  PublisherDOI

• Modeling and Optimizing the Provisioning of Exhaustible Capabilities for Simultaneous Task Allocation and Scheduling

  Open MIND · 2026-02-14

  preprintSenior author

  Deploying heterogeneous robot teams to accomplish multiple tasks over extended time horizons presents significant computational challenges for task allocation and planning. In this paper, we present a comprehensive, time-extended, offline heterogeneous multi-robot task allocation framework, TRAITS, which we believe to be the first that can cope with the provisioning of exhaustible traits under battery and temporal constraints. Specifically, we introduce a nonlinear programming-based trait distribution module that can optimize the trait-provisioning rate of coalitions to yield feasible and time-efficient solutions. TRAITS provides a more accurate feasibility assessment and estimation of task execution times and makespan by leveraging trait-provisioning rates while optimizing battery consumption -- an advantage that state-of-the-art frameworks lack. We evaluate TRAITS against two state-of-the-art frameworks, with results demonstrating its advantage in satisfying complex trait and battery requirements while remaining computationally tractable.

  DOI

• Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion

  ArXiv.org · 2025-06-24

  preprintOpen access

  We propose a novel hierarchical reinforcement learning framework for quadruped locomotion over challenging terrain. Our approach incorporates a two-layer hierarchy in which a high-level policy (HLP) selects optimal goals for a low-level policy (LLP). The LLP is trained using an on-policy actor-critic RL algorithm and is given footstep placements as goals. We propose an HLP that does not require any additional training or environment samples and instead operates via an online optimization process over the learned value function of the LLP. We demonstrate the benefits of this framework by comparing it with an end-to-end reinforcement learning (RL) approach. We observe improvements in its ability to achieve higher rewards with fewer collisions across an array of different terrains, including terrains more difficult than any encountered during training.

  PublisherOA PDFDOI

• “Data will solve robotics and automation: True or false?”: A debate

  Science Robotics · 2025-08-27

  review

  Leading researchers debate the long-term influence of model-free methods that use large sets of demonstration data to train numerical generative models to control robots.

  PublisherDOI

• PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects

  Lecture notes in computer science · 2025-01-01 · 3 citations

  book-chapterOpen access
  PublisherOA PDFDOI

• RL-augmented Adaptive Model Predictive Control for Bipedal Locomotion over Challenging Terrain

  ArXiv.org · 2025-09-22

  preprintOpen access

  Model predictive control (MPC) has demonstrated effectiveness for humanoid bipedal locomotion; however, its applicability in challenging environments, such as rough and slippery terrain, is limited by the difficulty of modeling terrain interactions. In contrast, reinforcement learning (RL) has achieved notable success in training robust locomotion policies over diverse terrain, yet it lacks guarantees of constraint satisfaction and often requires substantial reward shaping. Recent efforts in combining MPC and RL have shown promise of taking the best of both worlds, but they are primarily restricted to flat terrain or quadrupedal robots. In this work, we propose an RL-augmented MPC framework tailored for bipedal locomotion over rough and slippery terrain. Our method parametrizes three key components of single-rigid-body-dynamics-based MPC: system dynamics, swing leg controller, and gait frequency. We validate our approach through bipedal robot simulations in NVIDIA IsaacLab across various terrains, including stairs, stepping stones, and low-friction surfaces. Experimental results demonstrate that our RL-augmented MPC framework produces significantly more adaptive and robust behaviors compared to baseline MPC and RL.

  PublisherOA PDFDOI

• Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion

  2025-07-08

  article

  We propose a novel hierarchical reinforcement learning framework for quadruped locomotion over challenging terrain. Our approach incorporates a two-layer hierarchy in which a high-level policy (HLP) selects optimal goals for a low-level policy (LLP). The LLP is trained using an on-policy actor-critic RL algorithm and is given footstep placements as goals. We propose an HLP that does not require any additional training or environment samples and instead operates via an online optimization process over the learned value function of the LLP. We demonstrate the benefits of this framework by comparing it with an end-to-end reinforcement learning (RL) approach. We observe improvements in its ability to achieve higher rewards with fewer collisions across an array of different terrains, including terrains more difficult than any encountered during training.

  PublisherDOI

• Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests

  ArXiv.org · 2025-05-15

  preprintOpen access

  Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected disturbances such as asymmetric drag and an off-center payload. We assess the agent's performance under these perturbations in both simulation and real-world experiments, quantifying performance degradation and benchmarking it against an MPC baseline. Results indicate that the DRL agent performs reliably despite significant disturbances. Along with the open-source release of our implementation, we provide insights into effective training strategies, real-world challenges, and practical considerations for deploying DRLbased ASV controllers.

  PublisherOA PDFDOI

• Dependent Reachable Sets for the Constant Bearing Pursuit Strategy

  ArXiv.org · 2025-11-29

  preprintOpen accessSenior author

  This paper introduces a novel reachability problem for the scenario involving two agents, where one agent follows another agent using a feedback strategy. The geometry of the reachable set for an agent, termed \emph{dependent reachable set}, is characterized using the constant bearing pursuit strategy as a case study. Key theoretical results are presented that provide geometric bounds for the associated dependent reachable set. Simulation results are presented to empirically establish the shape of the dependent reachable set. In the process, an original optimization problem is formulated and analyzed for the constant bearing pursuit strategy.

  PublisherOA PDFDOI

Recent grants

• RI:Small: Capturing, Perceiving, and Rendering of Artistic Skills for Real-time Interactive Creation of Art

  NSF · $449k · 2020–2024

• NSF-CONACYT Collaborative Research: Search, Surveillance, and Pursuit by Autonomous Robots

  NSF · $100k · 2007–2010

• NRI/Collaborative Research: Improving the Safety and Agility of Robotic Flight with Bat-Inspired Flexible-Winged Robots

  NSF · $1.5M · 2014–2018

Frequent coauthors

• Soon‐Jo Chung

  California Institute of Technology

  27 shared

• Steven M. LaValle

  University of Oulu

  21 shared

• Rafael Murrieta-Cid

  Mathematics Research Center

  18 shared

• Gennaro Notomista

  University of Waterloo

  17 shared

• Magnus Egerstedt

  16 shared

• Line Garnero

  15 shared

• Nicholas Gans

  15 shared

• Sourabh Bhattacharya

  Iowa State University

  15 shared

Education

• PhD

  Purdue University