229 citations · 329 across the 32 of their papers we have counts for
6 papers · 1 filter
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents
Yash Shukla, Wenchang Gao, Vasanth Sarathy +3
Recent advancements in reasoning abilities of Large Language Models (LLM) has promoted their usage in problems that require high-level planning for robots and artificial agents. Ho…
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning
Yash Shukla, Bharat Kesari, Shivam Goel +2
Despite recent progress in Reinforcement Learning for robotics applications, many tasks remain prohibitively difficult to solve because of the expensive interaction cost. Transfer…
MOSAIC: Learning Unified Multi-Sensory Object Property Representations for Robot Learning via Interactive Perception
Gyan Tatiya, Jonathan Francis, Ho-Hsiang Wu +2
A holistic understanding of object properties across diverse sensory modalities (e.g., visual, audio, and haptic) is essential for tasks ranging from object categorization to compl…
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
Yash Shukla, Abhishek Kulkarni, Robert Wright +2
Despite advances in Reinforcement Learning, many sequential decision making tasks remain prohibitively expensive and impractical to learn. Recently, approaches that automatically g…
Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments
Tung Thai, Ming Shen, Mayank Garg +10
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 perf…
Cross-Tool and Cross-Behavior Perceptual Knowledge Transfer for Grounded Object Recognition
Gyan Tatiya, Jonathan Francis, Jivko Sinapov
Humans learn about objects via interaction and using multiple perceptions, such as vision, sound, and touch. While vision can provide information about an object's appearance, non-…