1 code implementation • 24 Mar 2024 • Manisha Natarajan, Chunyue Xue, Sanne van Waveren, Karen Feigh, Matthew Gombolay
For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly.
no code implementations • 30 Jan 2024 • Qingyu Xiao, Zulfiqar Zaidi, Matthew Gombolay
The rapid and precise localization and prediction of a ball are critical for developing agile robots in ball sports, particularly in sports like tennis characterized by high-speed ball movements and powerful spins.
1 code implementation • 12 Jul 2023 • Sean Ye, Manisha Natarajan, Zixuan Wu, Matthew Gombolay
Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limited.
1 code implementation • 30 Jan 2023 • Batuhan Altundas, Zheyuan Wang, Joshua Bishop, Matthew Gombolay
We propose a deep learning-based framework, called HybridNet, combining a heterogeneous graph-based encoder with a recurrent schedule propagator for scheduling stochastic human-robot teams under upper- and lower-bound temporal constraints.
no code implementations • 17 Jan 2023 • Lakshita Dodeja, Pradyumna Tambwekar, Erin Hedlund-Botti, Matthew Gombolay
While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems.
no code implementations • 13 Jan 2023 • Pradyumna Tambwekar, Matthew Gombolay
Our findings highlight internal consistency issues wherein participants perceived language explanations to be significantly more usable, however participants were better able to objectively understand the decision making process of the car through a decision tree explanation.
Decision Making Explainable Artificial Intelligence (XAI) +1
no code implementations • 6 Dec 2022 • Yue Yang, Letian Chen, Matthew Gombolay
Learning from Demonstration (LfD) is a powerful method for enabling robots to perform novel tasks as it is often more tractable for a non-roboticist end-user to demonstrate the desired skill and for the robot to efficiently learn from the associated data than for a human to engineer a reward function for the robot to learn the skill via reinforcement learning (RL).
no code implementations • 7 Oct 2022 • Andrew Silva, Pradyumna Tambwekar, Matthew Gombolay
Federated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server.
no code implementations • 24 Sep 2022 • Letian Chen, Sravan Jayanthi, Rohan Paleja, Daniel Martin, Viacheslav Zakharov, Matthew Gombolay
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.
1 code implementation • NeurIPS 2021 • Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, Matthew Gombolay
On the other hand, expert performance degrades with the addition of xAI-based support ($p<0. 05$), indicating that the cost of paying attention to the xAI outweighs the benefits obtained from being provided additional information to enhance SA.
1 code implementation • 17 Aug 2022 • Pradyumna Tambwekar, Lakshita Dodeja, Nathan Vaska, Wei Xu, Matthew Gombolay
Leveraging a game environment, we collect a dataset of over 1000 examples, mapping language strategies to the corresponding goals and constraints, and show that our model, trained on this dataset, significantly outperforms human interpreters in inferring strategic intent (i. e., goals and constraints) from language (p < 0. 05).
no code implementations • 23 Jul 2022 • Andrew Hundt, William Agnew, Vicky Zeng, Severin Kacianka, Matthew Gombolay
Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14].
no code implementations • 21 Jun 2022 • Esmaeil Seraj, Andrew Silva, Matthew Gombolay
Our approach enables UAVs to infer the latent fire propagation dynamics for time-extended coordination in safety-critical conditions.
no code implementations • 23 Mar 2022 • Max Zuo, Logan Schick, Matthew Gombolay, Nakul Gopalan
In each test, CA-RRT reached more states on average in the same number of iterations as weighted-RRT.
no code implementations • 14 Feb 2022 • Sravan Jayanthi, Letian Chen, Matthew Gombolay
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.
1 code implementation • 4 Feb 2022 • Rohan Paleja, Yaru Niu, Andrew Silva, Chace Ritchie, Sugju Choi, Matthew Gombolay
While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD).
1 code implementation • ICLR 2022 • Sachin Konan, Esmaeil Seraj, Matthew Gombolay
Information sharing is key in building team cognition and enables coordination and cooperation.
1 code implementation • Conference On Robot Learning (CoRL) 2021 • Andrew Hundt, Aditya Murali, Priyanka Hubli, Ran Liu, Nakul Gopalan, Matthew Gombolay, Gregory D. Hager
Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-specific training.
2 code implementations • Conference On Robot Learning (CoRL) 2021 • Elias Stengel-Eskin, Andrew Hundt, Zhuohong He, Aditya Murali, Nakul Gopalan, Matthew Gombolay, Gregory Hager
Our model completes block manipulation tasks with synthetic commands 530 more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning.
no code implementations • 9 Oct 2021 • Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan, Steven James, Matthew Gombolay, Benjamin Rosman
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions.
no code implementations • 8 Oct 2021 • Letian Chen, Rohan Paleja, Matthew Gombolay
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform novel tasks by providing demonstrations.
no code implementations • NAACL 2021 • Andrew Silva, Pradyumna Tambwekar, Matthew Gombolay
The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users.
1 code implementation • 18 Jan 2021 • Pradyumna Tambwekar, Andrew Silva, Nakul Gopalan, Matthew Gombolay
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy.
1 code implementation • 7 Dec 2020 • Qian Luo, Jing Wu, Matthew Gombolay
Learning from demonstration (LfD) is a powerful learning method to enable a robot to infer how to perform a task given one or more human demonstrations of the desired task.
Robotics
1 code implementation • 3 Dec 2020 • Andrew Silva, Rohit Chopra, Matthew Gombolay
As machine learning is increasingly deployed in the real world, it is paramount that we develop the tools necessary to analyze the decision-making of the models we train and deploy to end-users.
1 code implementation • 31 Oct 2020 • Esmaeil Seraj, Xiyang Wu, Matthew Gombolay
The FireCommander environment can be useful for research topics spanning a wide range of applications from Reinforcement Learning (RL) and Learning from Demonstration (LfD), to Coordination, Psychology, Human-Robot Interaction (HRI) and Teaming.
1 code implementation • 17 Oct 2020 • Letian Chen, Rohan Paleja, Matthew Gombolay
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration.
no code implementations • 3 Jul 2020 • Ruisen Liu, Manisha Natarajan, Matthew Gombolay
As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive.
no code implementations • 14 Jun 2020 • Esmaeil Seraj, Matthew Gombolay
Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path.
no code implementations • 27 Jan 2020 • Rohan Paleja, Matthew Gombolay
This inference requires the robot to be able to detect and classify the heterogeneity of its partners.
no code implementations • 2 Jan 2020 • Letian Chen, Rohan Paleja, Muyleng Ghuy, Matthew Gombolay
On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations.
no code implementations • 17 Dec 2019 • Mariah Schrum, Matthew Gombolay
Detecting and adapting to catastrophic failures in robotic systems requires a robot to learn its new dynamics quickly and safely to best accomplish its goals.
no code implementations • 4 Dec 2019 • Zheyuan Wang, Matthew Gombolay
Increasing interest in integrating advanced robotics within manufacturing has spurred a renewed concentration in developing real-time scheduling solutions to coordinate human-robot collaboration in this environment.
1 code implementation • NeurIPS 2020 • Rohan Paleja, Andrew Silva, Letian Chen, Matthew Gombolay
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints.
2 code implementations • 22 Mar 2019 • Andrew Silva, Taylor Killian, Ivan Dario Jimenez Rodriguez, Sung-Hyun Son, Matthew Gombolay
Decision trees are ubiquitous in machine learning for their ease of use and interpretability.
1 code implementation • 16 Mar 2019 • Esmaeil Seraj, Andrew Silva, Matthew Gombolay
Wildfires are destructive and inflict massive, irreversible harm to victims' lives and natural resources.
no code implementations • 14 Mar 2019 • Rohan Paleja, Matthew Gombolay
For assistive robots and virtual agents to achieve ubiquity, machines will need to anticipate the needs of their human counterparts.
1 code implementation • 15 Feb 2019 • Andrew Silva, Matthew Gombolay
Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation.
no code implementations • 11 May 2018 • Matthew Gombolay, Reed Jensen, Jessica Stigile, Toni Golen, Neel Shah, Sung-Hyun Son, Julie Shah
We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem.