Demo Data from EDBT 2026 / reviewers in the wild / expert
Kunal Pratap Singh
dblp:243/8289
· DBLP profile ↗
7ranked-venue papers
3as first author
5since 2021 · last 2024
0000-0003-3113-950XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 6 · 3 first-author · 5 since 2021Graphics, computer vision, multimedia, augmented reality and games · 5 · 2 first-author · 3 since 2021Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
6 papers |
Robot navigation and mapping · 40% Motion planning and robot control · 25% Reinforcement learning · 15% |
Topics — the 11 heaviest of 13, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
object goal navigation |
1.2 | 2 | 2023 | Scene Graph Contrastive Learning for Embodied Navigation · ICCV 2023 Ask4Help: Learning to Leverage an Expert for Embodied Tasks · NeurIPS 2022 |
Robotics › Robot navigation and mapping
embodied navigation |
1.2 | 2 | 2023 | Scene Graph Contrastive Learning for Embodied Navigation · ICCV 2023 Factorizing Perception and Policy for Interactive Instruction Following · ICCV 2021 |
Robotics › Motion planning and robot control › robot learning › robot policy learning
generalist robot policy |
0.8 | 1 | 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 |
Machine learning › Reinforcement learning
imitation learning |
0.8 | 1 | 2024 | SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World · CVPR 2024 |
Robotics › Motion planning and robot control › robot learning › manipulation learning
language-conditioned manipulation |
0.8 | 1 | 2024 | SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World · CVPR 2024 |
Robotics › Motion planning and robot control
robot learning |
0.8 | 1 | 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 |
Robotics › Robot navigation and mapping
visual navigation |
0.8 | 1 | 2024 | SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World · CVPR 2024 |
Machine learning › Representation and self-supervised learning
contrastive learning |
0.7 | 1 | 2023 | Scene Graph Contrastive Learning for Embodied Navigation · ICCV 2023 |
Machine learning › Reinforcement learning
embodied agent training |
0.6 | 1 | 2022 | Ask4Help: Learning to Leverage an Expert for Embodied Tasks · NeurIPS 2022 |
Robotics › Robot navigation and mapping › visual navigation
language-guided navigation |
0.5 | 1 | 2021 | Factorizing Perception and Policy for Interactive Instruction Following · ICCV 2021 |
Machine learning › Efficient and distributed learning
model compression |
0.4 | 1 | 2020 | Learning Architectures for Binary Networks · ECCV (12) 2020 |
Methods — techniques the papers use, named apart from their topics
transformer policy · 0.8transformer architecture · 0.8shortest-path planning · 0.8large-scale pretraining · 0.8image augmentation · 0.8scene graph · 0.7contrastive learning · 0.7reinforcement learning · 0.6policy learning · 0.6modular architecture · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real WorldabstractReinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents. RL requires extensive reward shaping and auxiliary losses and is often too slow and ineffective for long-horizon tasks. While IL with human supervision is effective, collecting human trajectories at scale is extremely expensive. In this work, we show that imitating shortest-path planners in simulation produces agents that, given a language instruction, can proficiently navigate, ex-plore, and manipulate objects in both simulation and in the real world using only RGB sensors (no depth map or GPS coordinates). This surprising result is enabled by our end-to-end, transformer-based, Spocarchitecture, power-ful visual encoders paired with extensive image augmentation, and the dramatic scale and diversity of our training data: millions of frames of shortest-path-expert trajectories collected inside approximately 200,000 procedu-rally generated houses containing 40,000 unique 3D as-sets. Our models, data, training code, and newly proposed 10-task benchmarking suite Choresare available in spoc-robot.github.io. Kiana Ehsani, Tanmay Gupta, Rose Hendrix, Jordi Salvador, Luca Weihs, Kuo-Hao Zeng, Kunal Pratap Singh, Yejin Kim 0003, Winson Han, Alvaro Herrasti, Ranjay Krishna, Dustin Schwenk, Eli VanderBilt, Aniruddha Kembhavi |
CVPR | 7 |
| 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment CollaborationabstractLarge, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin |
ICRA | 137 |
| 2023 | Scene Graph Contrastive Learning for Embodied NavigationabstractTraining effective embodied AI agents often involves expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization. Another approach is to use neural architectures alongside self-supervised objectives which encourage better representation learning. However, in practice, there are few guarantees that these self-supervised objectives encode task-relevant information. We propose the Scene Graph Contrastive (SGC) loss, which uses scene graphs as training-only supervisory signals. The SGC loss does away with explicit graph decoding and instead uses contrastive learning to align an agent’s representation with a rich graphical encoding of its environment. The SGC loss is simple to implement and encourages representations that encode objects’ semantics, relationships, and history. By using the SGC loss, we attain gains on three embodied tasks: Object Navigation, Multi-Object Navigation, and Arm Point Navigation. Finally, we present studies and analyses which demonstrate the ability of our trained representation to encode semantic cues about the environment. Kunal Pratap Singh, Jordi Salvador, Luca Weihs, Aniruddha Kembhavi |
ICCV | 1 |
| 2022 | Ask4Help: Learning to Leverage an Expert for Embodied TasksabstractEmbodied AI agents continue to become more capable every year with the advent of new models, environments, and benchmarks, but are still far away from being performant and reliable enough to be deployed in real, user-facing, applications. In this paper, we ask: can we bridge this gap by enabling agents to ask for assistance from an expert such as a human being? To this end, we propose the Ask4Help policy that augments agents with the ability to request, and then use expert assistance. Ask4Help policies can be efficiently trained without modifying the original agent's parameters and learn a desirable trade-off between task performance and the amount of requested help, thereby reducing the cost of querying the expert. We evaluate Ask4Help on two different tasks -- object goal navigation and room rearrangement and see substantial improvements in performance using minimal help. On object navigation, an agent that achieves a $52\%$ success rate is raised to $86\%$ with $13\%$ help and for rearrangement, the state-of-the-art model with a $7\%$ success rate is dramatically improved to $90.4\%$ using $39\%$ help. Human trials with Ask4Help demonstrate the efficacy of our approach in practical scenarios. Kunal Pratap Singh, Luca Weihs, Alvaro Herrasti, Aniruddha Kembhavi, Roozbeh Mottaghi |
NeurIPS | 1 |
| 2021 | Factorizing Perception and Policy for Interactive Instruction FollowingabstractPerforming simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents. The ‘interactive instruction following’ task attempts to make progress towards building agents that jointly navigate, interact, and reason in the environment at every step. To address the multifaceted problem, we propose a model that factorizes the task into interactive perception and action policy streams with enhanced components and name it as MOCA, a Modular Object-Centric Approach. We empirically validate that MOCA outperforms prior arts by significant margins on the ALFRED benchmark with improved generalization. Kunal Pratap Singh, Suvaansh Bhambri, Byeonghwi Kim, Roozbeh Mottaghi |
ICCV | 1 |
| 2020 | Learning Architectures for Binary Networks
Dahyun Kim 0001, Kunal Pratap Singh |
ECCV (12) | 2 |
| 2019 | A Fast, Scalable, and Reliable Deghosting Method for Extreme Exposure FusionabstractHDR fusion of extreme exposure images with complex camera and object motion is a challenging task. Existing patch-based optimization techniques generate noisy and/or blurry results with undesirable artifacts for difficult scenarios. Additionally, they are computationally intensive and have high execution times. Recently proposed CNN-based methods offer fast alternatives, but still fail to generate artifact-free results for extreme exposure images. Furthermore, they do not scale to an arbitrary number of input images. To address these issues, we propose a simple, yet effective CNN-based multi-exposure image fusion method that produces artifact-free HDR images. Our method is fast, and scales to an arbitrary number of input images. Additionally, we prepare a large dataset of 582 varying exposure images with corresponding deghosted HDR images to train our model. We test the efficacy of our algorithm on publicly available datasets, and achieve significant improvements over existing state-of-the-art methods. Through experimental results, we demonstrate that our method produces artifact-free results, and offers a speed-up of around 54× over existing state-of-the-art HDR fusion methods. K. Ram Prabhakar, Rajat Arora 0002, Adhitya Swaminathan, Kunal Pratap Singh, Venkatesh Babu Radhakrishnan |
ICCP | 4 |