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

1 code implementation • 31 Oct 2024 • Matthew Chang, Gunjan Chhablani, Alexander Clegg, Mikael Dallaire Cote, Ruta Desai, Michal Hlavac, Vladimir Karashchuk, Jacob Krantz, Roozbeh Mottaghi, Priyam Parashar, Siddharth Patki, Ishita Prasad, Xavier Puig, Akshara Rai, Ram Ramrakhya, Daniel Tran, Joanne Truong, John M. Turner, Eric Undersander, Tsung-Yen Yang

We present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities.

Situated Instruction Following

no code implementations • 15 Jul 2024 • So Yeon Min, Xavi Puig, Devendra Singh Chaplot, Tsung-Yen Yang, Akshara Rai, Priyam Parashar, Ruslan Salakhutdinov, Yonatan Bisk, Roozbeh Mottaghi

Language is never spoken in a vacuum.

Instruction Following

Spatial-Language Attention Policies for Efficient Robot Learning

no code implementations • 21 Apr 2023 • Priyam Parashar, Vidhi Jain, Xiaohan Zhang, Jay Vakil, Sam Powers, Yonatan Bisk, Chris Paxton

We see a 4x improvement over baseline in mobile manipulation setting.

Decision Making Language Modelling +1

Meta-Modeling of Assembly Contingencies and Planning for Repair

no code implementations • 12 Mar 2021 • Priyam Parashar, Aayush Naik, Jiaming Hu, Henrik I. Christensen

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 environment.

Pose Estimation of Specular and Symmetrical Objects

no code implementations • 31 Oct 2020 • Jiaming Hu, Hongyi Ling, Priyam Parashar, Aayush Naik, Henrik Christensen

In the robotic industry, specular and textureless metallic components are ubiquitous.

6D Pose Estimation Template Matching

Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations

no code implementations • 1 Jun 2018 • Priyam Parashar, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura

This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios.

Decision Making