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Heramb Nemlekar

10 accepted papers

2025

RECON: Reducing Causal Confusion with Human-Placed Markers

IROS 2025

Imitation learning enables robots to learn new tasks from human examples. One fundamental limitation while learning from humans is causal confusion. Causal confusion occurs when the robot’s observations include both task-relevant and extraneous information: for instance, a robot’s camera might see n

Cited by 3SourceScholar
2024

Multi-Robot Task Allocation Under Uncertainty Via Hindsight Optimization

ICRA 2024poster

Multi-robot systems are becoming increasingly prevalent in various real-world applications, such as manufacturing and warehouse logistics. These systems face complex challenges in 1) task allocation due to factors like time-extended tasks, and agent specialization, and 2) uncertainties in task execu…

Cited by 2SourceScholar
2024

Selecting Source Tasks for Transfer Learning of Human Preferences

RA-L 2024

We address the challenge of transferring human preferences for action selection from simpler source tasks to complex target tasks. Our goal is to enable robots to support humans proactively by predicting their actions — without requiring demonstrations of their preferred action sequences in the targ

Cited by 0SourceScholar
2023

Surrogate Assisted Generation of Human-Robot Interaction Scenarios

CoRL 2023oral

As human-robot interaction (HRI) systems advance, so does the difficulty of evaluating and understanding the strengths and limitations of these systems in different environments and with different users. To this end, previous methods have algorithmically generated diverse scenarios that reveal syste…

Cited by 11SourcecodeScholar
2021

Robotic Lime Picking by Considering Leaves as Permeable Obstacles

IROS 2021poster

The problem of robotic lime picking is challenging; lime plants have dense foliage which makes it difficult for a robotic arm to grasp a lime without coming in contact with leaves. Existing approaches either do not consider leaves, or treat them as obstacles and completely avoid them, often resultin…

Cited by 19SourceScholar
2021

Two-Stage Clustering of Human Preferences for Action Prediction in Assembly Tasks

ICRA 2021poster

To effectively assist human workers in assembly tasks a robot must proactively offer support by inferring their preferences in sequencing the task actions. Previous work has focused on learning the dominant preferences of human workers for simple tasks largely based on their intended goal. However,…

Cited by 13SourceScholar
2020

Fair Contextual Multi-Armed Bandits: Theory and Experiments

UAI 2020poster

When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group, or a factory robot selects a worker to deliver a part.Demonstrating fairness in decision making is essential for such systems…

Cited by 79SourcePDFScholar