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Aditya Agarwal

IIIT Hyderabad

13 papers hereh-index 7614 citations20 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author9

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV7
  • cs.RO6
affiliations
  • IIIT Hyderabad
  • MIT
  • Mila Quebec AI Institute
Homepage
same name
  • Aditya Agarwal — 2 papers
  • Aditya Agarwal — 1 paper, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212026
most citedEDMP: Ensemble-of-costs-guided Diffusion for Motion Planning

25 citations · 46 across the 13 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.RO2023★ 12 cited

ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning

Qiao Gu, Alihusein Kuwajerwala, Sacha Morin +13

For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and efficient for task-driven perception and pla…

cs.RO2023★ 25 cited

EDMP: Ensemble-of-costs-guided Diffusion for Motion Planning

Kallol Saha, Vishal Mandadi, Jayaram Reddy +5

Classical motion planning for robotic manipulation includes a set of general algorithms that aim to minimize a scene-specific cost of executing a given plan. This approach offers r…

cs.CV2023

HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork

Bipasha Sen, Gaurav Singh, Aditya Agarwal +3

Neural Radiance Fields (NeRF) have become an increasingly popular representation to capture high-quality appearance and shape of scenes and objects. However, learning generalizable…

cs.CV2023★ 2 cited

SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping

Bipasha Sen, Aditya Agarwal, Gaurav Singh +3

Recovering full 3D shapes from partial observations is a challenging task that has been extensively addressed in the computer vision community. Many deep learning methods tackle th…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.