no code implementations • 1 Apr 2024 • Casey Kennington, Malihe Alikhani, Heather Pon-Barry, Katherine Atwell, Yonatan Bisk, Daniel Fried, Felix Gervits, Zhao Han, Mert Inan, Michael Johnston, Raj Korpan, Diane Litman, Matthew Marge, Cynthia Matuszek, Ross Mead, Shiwali Mohan, Raymond Mooney, Natalie Parde, Jivko Sinapov, Angela Stewart, Matthew Stone, Stefanie Tellex, Tom Williams
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
1 code implementation • 6 Feb 2024 • Yash Shukla, Tanushree Burman, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov
In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions.
no code implementations • 7 Jan 2024 • Shivam Goel, Yichen Wei, Panagiotis Lymperopoulos, Matthias Scheutz, Jivko Sinapov
To this end, we introduce NovelGym, a flexible and adaptable ecosystem designed to simulate gridworld environments, serving as a robust platform for benchmarking reinforcement learning (RL) and hybrid planning and learning agents in open-world contexts.
no code implementations • 14 Oct 2023 • Yash Shukla, Wenchang Gao, Vasanth Sarathy, Alvaro Velasquez, Robert Wright, Jivko Sinapov
In this work, we propose LgTS (LLM-guided Teacher-Student learning), a novel approach that explores the planning abilities of LLMs to provide a graphical representation of the sub-goals to a reinforcement learning (RL) agent that does not have access to the transition dynamics of the environment.
1 code implementation • 13 Oct 2023 • Yash Shukla, Bharat Kesari, Shivam Goel, Robert Wright, Jivko Sinapov
We use Generative Adversarial Networks (GANs) along with a cycle-consistency loss to map the observations between the source and target domains and later use this learned mapping to clone the successful source task behavior policy to the target domain.
1 code implementation • 11 Apr 2023 • Yash Shukla, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov
Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e. g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e. g, Q-Learning for Reward Machines).
no code implementations • 28 Feb 2023 • Tung Thai, Ming Shen, Mayank Garg, Ayush Kalani, Nakul Vaidya, Utkarsh Soni, Mudit Verma, Sriram Gopalakrishnan, Neeraj Varshney, Chitta Baral, Subbarao Kambhampati, Jivko Sinapov, Matthias Scheutz
Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task performance.
no code implementations • 21 Dec 2022 • Gyan Tatiya, Jonathan Francis, Luca Bondi, Ingrid Navarro, Eric Nyberg, Jivko Sinapov, Jean Oh
We also define a new audio-visual navigation sub-task, where agents are evaluated on novel sounding objects, as opposed to unheard clips of known objects.
1 code implementation • 24 Jun 2022 • Shivam Goel, Yash Shukla, Vasanth Sarathy, Matthias Scheutz, Jivko Sinapov
We propose RAPid-Learn: Learning to Recover and Plan Again, a hybrid planning and learning method, to tackle the problem of adapting to sudden and unexpected changes in an agent's environment (i. e., novelties).
no code implementations • 21 Apr 2022 • Evana Gizzi, Lakshmi Nair, Sonia Chernova, Jivko Sinapov
Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems.
no code implementations • 10 Oct 2021 • Ziyi Zhang, Samuel Micah Akai-Nettey, Adonai Addo, Chris Rogers, Jivko Sinapov
To create a common ground between the human and the learning robot, in this paper, we propose an Augmented Reality (AR) system that reveals the hidden state of the learning to the human users.
no code implementations • 22 Sep 2021 • Reuth Mirsky, Megan Zimmerman, Muneed Ahmad, Shelly Bagchi, Felix Gervits, Zhao Han, Justin Hart, Daniel Hernández García, Matteo Leonetti, Ross Mead, Emmanuel Senft, Jivko Sinapov, Jason Wilson
In addition, acknowledging that ethics is an inherent part of the human-robot interaction, we encourage submissions of works on ethics for HRI.
1 code implementation • 15 Sep 2021 • Xiaohui Chen, Ramtin Hosseini, Karen Panetta, Jivko Sinapov
The framework was tested and validated with a dataset containing 4 sensory modalities (vision, haptic, audio, and tactile) on a humanoid robot performing 9 behaviors multiple times on a large set of objects.
no code implementations • 24 Dec 2020 • Vasanth Sarathy, Daniel Kasenberg, Shivam Goel, Jivko Sinapov, Matthias Scheutz
Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains.
no code implementations • 26 Oct 2020 • Shelly Bagchi, Jason R. Wilson, Muneeb I. Ahmad, Christian Dondrup, Zhao Han, Justin W. Hart, Matteo Leonetti, Katrin Lohan, Ross Mead, Emmanuel Senft, Jivko Sinapov, Megan L. Zimmerman
We see a growing need for research that lives directly at the intersection of AI and HRI that is serviced by this symposium.
no code implementations • 10 Mar 2020 • Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback.
no code implementations • 11 Sep 2019 • Justin W. Hart, Nick DePalma, Richard G. Freedman, Luca Iocchi, Matteo Leonetti, Katrin Lohan, Ross Mead, Emmanuel Senft, Jivko Sinapov, Elin A. Topp, Tom Williams
The past few years have seen rapid progress in the development of service robots.
Robotics
1 code implementation • 1 Mar 2019 • Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, Raymond J. Mooney
Natural language understanding for robotics can require substantial domain- and platform-specific engineering.
no code implementations • WS 2017 • Jesse Thomason, Jivko Sinapov, Raymond Mooney
Multi-modal grounded language learning connects language predicates to physical properties of objects in the world.