Search Results for author: Ayush Agrawal

Found 6 papers, 2 papers with code

Physical Reasoning and Object Planning for Household Embodied Agents

1 code implementation22 Nov 2023 Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal, Dianbo Liu

In this study, we explore the sophisticated domain of task planning for robust household embodied agents, with a particular emphasis on the intricate task of selecting substitute objects.

Decision Making Object +1

Do Language Models Know When They're Hallucinating References?

1 code implementation29 May 2023 Ayush Agrawal, Mirac Suzgun, Lester Mackey, Adam Tauman Kalai

In this work, we focus on hallucinated book and article references and present them as the "model organism" of language model hallucination research, due to their frequent and easy-to-discern nature.

Hallucination Language Modelling +1

Sequence-Agnostic Multi-Object Navigation

no code implementations10 May 2023 Nandiraju Gireesh, Ayush Agrawal, Ahana Datta, Snehasis Banerjee, Mohan Sridharan, Brojeshwar Bhowmick, Madhava Krishna

The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes.

Object

Towards a Mathematics Formalisation Assistant using Large Language Models

no code implementations14 Nov 2022 Ayush Agrawal, Siddhartha Gadgil, Navin Goyal, Ashvni Narayanan, Anand Tadipatri

Mathematics formalisation is the task of writing mathematics (i. e., definitions, theorem statements, proofs) in natural language, as found in books and papers, into a formal language that can then be checked for correctness by a program.

Language Modelling Large Language Model

Autonomous Navigation for Quadrupedal Robots with Optimized Jumping through Constrained Obstacles

no code implementations1 Jul 2021 Scott Gilroy, Derek Lau, Lizhi Yang, Ed Izaguirre, Kristen Biermayer, Anxing Xiao, Mengti Sun, Ayush Agrawal, Jun Zeng, Zhongyu Li, Koushil Sreenath

The resulted jumping mode is utilized in an autonomous navigation pipeline that leverages a search-based global planner and a local planner to enable the robot to reach the goal location by walking.

Autonomous Navigation Decision Making +1

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