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Found 19 papers, 6 papers with code
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
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.
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
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.
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents
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.
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning
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.
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
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).
Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments
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.
Knowledge-driven Scene Priors for Semantic Audio-Visual Embodied Navigation
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.
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments
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).
Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework
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.
An Augmented Reality Platform for Introducing Reinforcement Learning to K-12 Students with Robots
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.
AI-HRI 2021 Proceedings
In addition, acknowledging that ethics is an inherent part of the human-robot interaction, we encourage submissions of works on ethics for HRI.
A Framework for Multisensory Foresight for Embodied Agents
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.
SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains.
Proceedings of the AI-HRI Symposium at AAAI-FSS 2020
We see a growing need for research that lives directly at the intersection of AI and HRI that is serviced by this symposium.
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback.
Proceedings of the AI-HRI Symposium at AAAI-FSS 2019
The past few years have seen rapid progress in the development of service robots.
Robotics
Improving Grounded Natural Language Understanding through Human-Robot Dialog
Natural language understanding for robotics can require substantial domain- and platform-specific engineering.
Guiding Interaction Behaviors for Multi-modal Grounded Language Learning
Multi-modal grounded language learning connects language predicates to physical properties of objects in the world.
