activity
20182026
most citedACuTE: Automatic Curriculum Transfer from Simple to Complex Environments

4 citations · 20 across the 19 of their papers we have counts for

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16 papers · 1 filter

cs.RO2026

An offline approach to fNIRS-guided reinforcement learning for robot behavior

Julia Santaniello, Madelaine Brower, Benson Jiang +4

Human-in-the-loop Reinforcement Learning has become a popular approach for training, finetuning, and aligning robot behavior with user preferences. Our paper explores the feasibili…

cs.RO2026

Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning

Hong Lu, Pierrick Lorang, Timothy R. Duggan +2

In dynamic open-world environments, autonomous agents often encounter novelties that hinder their ability to find plans to achieve their goals. Specifically, traditional symbolic p…

cs.RO2025

FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation

Shijie Fang, Wenchang Gao, Shivam Goel +3

Learning to manipulate objects efficiently, particularly those involving sustained contact (e.g., pushing, sliding) and articulated parts (e.g., drawers, doors), presents significa…

cs.RO2023

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…

cs.RO2023

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…

cs.RO2023

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-…