Siddharth Patki — accepted AI-conference papers

2025

PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks

Matthew Chang, Gunjan Chhablani, Alexander Clegg, Mikael Dallaire Cote, Ruta Desai, Michal Hlavac +14

ICLR 2025poster

We present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities. PARTNR tasks exhibit characteristics of everyday tasks, such as spatial, temporal, and heterogeneous agent capability constraints. We empl…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2023

Language Guided Temporally Adaptive Perception for Efficient Natural Language Grounding in Cluttered Dynamic Worlds

Siddharth Patki, Jacob Arkin, Nikola Raicevic, Thomas M. Howard

IROS 2023poster

As robots operate alongside humans in shared spaces, such as homes and offices, it is essential to have an effective mechanism for interacting with them. Natural language offers an intuitive interface for communicating with robots, but most of the recent approaches to grounded language understanding…

Cited by 3 Source ↗Scholar ↗

2019

Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions

Siddharth Patki, Andrea F. Daniele, Matthew R. Walter, Thomas M. Howard

ICRA 2019poster

The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been paid to developing models and approximate inference algorithms that improve the efficiency of language understanding. Howe…

Cited by 27 Source ↗Scholar ↗

2019

Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments

Siddharth Patki, Ethan Fahnestock, Thomas M. Howard, Matthew R. Walter

CoRL 2019

Recent advances in data-driven models for grounded language understanding have enabled robots to interpret increasingly complex instructions. Two fundamental limitations of these methods are that most require a full model of the environment to be known a priori, and they attempt to reason over a wor

Cited by 0 Source ↗PDF ↗Scholar ↗