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20182024
most citedLessons Learned Developing an Assembly System for WRS 2020 Assembly Challenge

1 citations · 3 across the 6 of their papers we have counts for

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cs.RO2024

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

Matthew Chang, Gunjan Chhablani, Alexander Clegg +17

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 exhib…

cs.RO2023★ 1 cited

Spatial-Language Attention Policies for Efficient Robot Learning

Priyam Parashar, Vidhi Jain, Xiaohan Zhang +4

Despite great strides in language-guided manipulation, existing work has been constrained to table-top settings. Table-tops allow for perfect and consistent camera angles, properti…

cs.RO2021★ 1 cited

Lessons Learned Developing an Assembly System for WRS 2020 Assembly Challenge

Aayush Naik, Priyam Parashar, Jiaming Hu +1

The World Robot Summit (WRS) 2020 Assembly Challenge is designed to allow teams to demonstrate how one can build flexible, robust systems for assembly of machined objects. We prese…

cs.RO2021

Meta-Modeling of Assembly Contingencies and Planning for Repair

Priyam Parashar, Aayush Naik, Jiaming Hu +1

The World Robotics Challenge (2018 & 2020) was designed to challenge teams to design systems that are easy to adapt to new tasks and to ensure robust operation in a semi-structured…

cs.RO2020★ 1 cited

Pose Estimation of Specular and Symmetrical Objects

Jiaming Hu, Hongyi Ling, Priyam Parashar +2

In the robotic industry, specular and textureless metallic components are ubiquitous. The 6D pose estimation of such objects with only a monocular RGB camera is difficult because o…

cs.RO2018

Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations

Priyam Parashar, Akansel Cosgun, Alireza Nakhaei +1

This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios. Simulation is used to re-create a targeted driv…