EDBT 2026 Demo / reviewers in the wild / expert
Lisa Lee
dblp:97/9403
· DBLP profile ↗
12ranked-venue papers
2as first author
6since 2021 · last 2024
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 12 · 2 first-author · 6 since 2021Graphics, computer vision, multimedia, augmented reality and games · 4Systems, architecture and hardware · 3 · 3 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
10 papers |
Reinforcement learning · 40% Motion planning and robot control · 13% Generative modeling · 9% |
Topics — the 30 heaviest of 33, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning
imitation learning |
1.4 | 2 | 2024 | Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation · ICRA 2024 Guide Your Agent with Adaptive Multimodal Rewards · NeurIPS 2023 |
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 › Learning theory › generalization error
generalization gap |
0.8 | 1 | 2024 | Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation · ICRA 2024 |
Machine learning › Generative modeling
image manipulation |
0.8 | 1 | 2024 | Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation · ICRA 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 |
Machine learning › Reinforcement learning › function approximation
representation learning for reinforcement learning |
0.7 | 1 | 2023 | Spectral Decomposition Representation for Reinforcement Learning · ICLR 2023 |
Machine learning › Reinforcement learning
reward design |
0.7 | 1 | 2023 | Guide Your Agent with Adaptive Multimodal Rewards · NeurIPS 2023 |
Machine learning › Reinforcement learning › function approximation › representation learning for reinforcement learning
spectral representation |
0.7 | 1 | 2023 | Spectral Decomposition Representation for Reinforcement Learning · ICLR 2023 |
Machine learning › Reinforcement learning › offline reinforcement learning
decision transformer |
0.6 | 1 | 2022 | Multi-Game Decision Transformers · NeurIPS 2022 |
Machine learning › Reinforcement learning
generalist agents |
0.6 | 1 | 2022 | Multi-Game Decision Transformers · NeurIPS 2022 |
Machine learning › Reinforcement learning
offline reinforcement learning |
0.6 | 1 | 2022 | Multi-Game Decision Transformers · NeurIPS 2022 |
Machine learning › Transfer learning and domain adaptation
cross-task transfer |
0.4 | 1 | 2020 | Embodied Multimodal Multitask Learning · IJCAI 2020 |
Natural language and speech › Language models and text generation › language acquisition
embodied language learning |
0.4 | 1 | 2020 | Embodied Multimodal Multitask Learning · IJCAI 2020 |
Computer vision › Vision and language
multimodal multitask learning |
0.4 | 1 | 2020 | Embodied Multimodal Multitask Learning · IJCAI 2020 |
Machine learning › Representation and self-supervised learning › representation learning › disentangled representation learning
weakly supervised disentanglement |
0.4 | 1 | 2020 | Weakly-Supervised Reinforcement Learning for Controllable Behavior · NeurIPS 2020 |
Machine learning › Deep learning architectures and training › recurrent neural network
gated recurrent network |
0.3 | 1 | 2018 | Gated Path Planning Networks · ICML 2018 |
Robotics › Motion planning and robot control
path planning |
0.3 | 1 | 2018 | Gated Path Planning Networks · ICML 2018 |
Machine learning › Deep learning architectures and training
recurrent neural network |
0.3 | 1 | 2018 | Gated Path Planning Networks · ICML 2018 |
Machine learning › Reinforcement learning › value-based reinforcement learning
value iteration network |
0.3 | 1 | 2018 | Gated Path Planning Networks · ICML 2018 |
Computer vision › Image recognition and object detection › attribute recognition
attribute detection |
0.3 | 1 | 2017 | Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection · CVPR 2017 |
Machine learning › Generative modeling
generative adversarial network |
0.3 | 1 | 2017 | Dual Motion GAN for Future-Flow Embedded Video Prediction · ICCV 2017 |
Machine learning › Learning paradigms › unsupervised learning
unsupervised video representation learning |
0.3 | 1 | 2017 | Dual Motion GAN for Future-Flow Embedded Video Prediction · ICCV 2017 |
Machine learning › Generative modeling
video generation |
0.3 | 1 | 2017 | Dual Motion GAN for Future-Flow Embedded Video Prediction · ICCV 2017 |
Computer vision › Video understanding and tracking
video prediction |
0.3 | 1 | 2017 | Dual Motion GAN for Future-Flow Embedded Video Prediction · ICCV 2017 |
Computer vision › Vision and language
visual relationship detection |
0.3 | 1 | 2017 | Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection · CVPR 2017 |
Machine learning › Transfer learning and domain adaptation
sim-to-real transfer |
0.2 | 1 | 2024 | Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation · ICRA 2024 |
Machine learning › Representation and self-supervised learning › multimodal representation learning
multimodal embedding |
0.2 | 1 | 2023 | Guide Your Agent with Adaptive Multimodal Rewards · NeurIPS 2023 |
Machine learning › Efficient and distributed learning › large-scale learning
model scaling |
0.2 | 1 | 2022 | Multi-Game Decision Transformers · NeurIPS 2022 |
Machine learning › Deep learning architectures and training
transformer |
0.2 | 1 | 2022 | Multi-Game Decision Transformers · NeurIPS 2022 |
Machine learning › Reinforcement learning
exploration |
0.1 | 1 | 2020 | Weakly-Supervised Reinforcement Learning for Controllable Behavior · NeurIPS 2020 |
Methods — techniques the papers use, named apart from their topics
transformer policy · 0.8large-scale pretraining · 0.8imitation learning · 0.8factor decomposition · 0.8benchmark design · 0.8spectral decomposition · 0.7multimodal encoder fine-tuning · 0.7CLIP · 0.7offline RL · 0.6behavioral cloning · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 | 148 |
| 2024 | Decomposing the Generalization Gap in Imitation Learning for Visual Robotic ManipulationabstractWhat makes generalization hard for imitation learning in visual robotic manipulation? This question is difficult to approach at face value, but the environment from the perspective of a robot can often be decomposed into enumerable factors of variation, such as the lighting conditions or the placement of the camera. Empirically, generalization to some of these factors have presented a greater obstacle than others, but existing work sheds little light on precisely how much each factor contributes to the generalization gap. Towards an answer to this question, we study imitation learning policies in simulation and on a real robot language-conditioned manipulation task to quantify the difficulty of generalization to different (sets of) factors. We design a simulated benchmark of 19 tasks with 11 factors of variation to facilitate more controlled evaluations of generalization. From our study, we determine an ordering of factors based on generalization difficulty, that is consistent across simulation and our real robot setup.1 Annie Xie, Lisa Lee, Ted Xiao, Chelsea Finn |
ICRA | 2 |
| 2024 | The Design of the Barkour Benchmark for Robot AgilityabstractIn this paper, we describe the design of the Barkour benchmark for measuring robot agility in navigating complex environments. Despite the growing interest in developing agile robot locomotion skills, the field lacks systematic benchmarks to measure the performance of robotic control systems and hardware in agility-focused tasks. This motivated us to propose the Barkour benchmark, an obstacle course designed to quantify agility across various robotic platforms. Inspired by dog agility competitions, the course features diverse obstacles and a time-based scoring mechanism, encouraging researchers to develop controllers that enable robots to move quickly, precisely, and with adaptability. This benchmark is challenging as it demands diverse motion skills and the time-based scoring requires control precision at high speed. Along with the design details presented in the paper, we release our simulated environment setups in MuJoCo-XLA and the CAD model of a custom-designed quadruped robot to facilitate future research to reproduce the Barkour setup (available at sites.google.com/view/barkour). We hope these together will accelerate the pace of robot agility research. Wenhao Yu 0003, Ken Caluwaerts, Atil Iscen, J. Chase Kew, Tingnan Zhang, Daniel Freeman, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, José Enrique Chen, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Tsang-Wei Edward Lee, Ofir Nachum, Kenneth Oslund, Francesco Romano, Fereshteh Sadeghi, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan 0001, Kuang-Huei Lee |
IROS | 7 |
| 2023 | Spectral Decomposition Representation for Reinforcement Learning
Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph Gonzalez 0001, Dale Schuurmans, Bo Dai 0001 |
ICLR | 3 |
| 2023 | Guide Your Agent with Adaptive Multimodal RewardsabstractDeveloping an agent capable of adapting to unseen environments remains a difficult challenge in imitation learning. This work presents Adaptive Return-conditioned Policy (ARP), an efficient framework designed to enhance the agent's generalization ability using natural language task descriptions and pre-trained multimodal encoders. Our key idea is to calculate a similarity between visual observations and natural language instructions in the pre-trained multimodal embedding space (such as CLIP) and use it as a reward signal. We then train a return-conditioned policy using expert demonstrations labeled with multimodal rewards. Because the multimodal rewards provide adaptive signals at each timestep, our ARP effectively mitigates the goal misgeneralization. This results in superior generalization performances even when faced with unseen text instructions, compared to existing text-conditioned policies. To improve the quality of rewards, we also introduce a fine-tuning method for pre-trained multimodal encoders, further enhancing the performance. Video demonstrations and source code are available on the project website: \url{https://sites.google.com/view/2023arp}. Changyeon Kim, Younggyo Seo, Lisa Lee, Jinwoo Shin, Honglak Lee, Kimin Lee |
NeurIPS | 4 |
| 2022 | Multi-Game Decision TransformersabstractA longstanding goal of the field of AI is a method for learning a highly capable, generalist agent from diverse experience. In the subfields of vision and language, this was largely achieved by scaling up transformer-based models and training them on large, diverse datasets. Motivated by this progress, we investigate whether the same strategy can be used to produce generalist reinforcement learning agents. Specifically, we show that a single transformer-based model – with a single set of weights – trained purely offline can play a suite of up to 46 Atari games simultaneously at close-to-human performance. When trained and evaluated appropriately, we find that the same trends observed in language and vision hold, including scaling of performance with model size and rapid adaptation to new games via fine-tuning. We compare several approaches in this multi-game setting, such as online and offline RL methods and behavioral cloning, and find that our Multi-Game Decision Transformer models offer the best scalability and performance. We release the pre-trained models and code to encourage further research in this direction. Kuang-Huei Lee, Ofir Nachum, Sherry Yang 0001, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, Igor Mordatch |
NeurIPS | 4 |
| 2020 | Embodied Multimodal Multitask LearningabstractVisually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the inability to transfer to new words and concepts not seen during training using only a few examples. We propose a multitask model which facilitates knowledge transfer across tasks by disentangling the knowledge of words and visual attributes in the intermediate representations. We create scenarios and datasets to quantify cross-task knowledge transfer and show that the proposed model outperforms a range of baselines in simulated 3D environments. We also show that this disentanglement of representations makes our model modular and interpretable which allows for transfer to instructions containing new concepts. Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov, Devi Parikh, Dhruv Batra |
IJCAI | 2 |
| 2020 | Weakly-Supervised Reinforcement Learning for Controllable BehaviorabstractReinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks. However, in many settings, an agent must winnow down the inconceivably large space of all possible tasks to the single task that it is currently being asked to solve. Can we instead constrain the space of tasks to those that are semantically meaningful? In this work, we introduce a framework for using weak supervision to automatically disentangle this semantically meaningful subspace of tasks from the enormous space of nonsensical "chaff" tasks. We show that this learned subspace enables efficient exploration and provides a representation that captures distance between states. On a variety of challenging, vision-based continuous control problems, our approach leads to substantial performance gains, particularly as the complexity of the environment grows. Lisa Lee, Benjamin Eysenbach, Ruslan Salakhutdinov, Shixiang Gu, Chelsea Finn |
NeurIPS | 1 |
| 2018 | Gated Path Planning NetworksabstractValue Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture. Despite their effectiveness, they suffer from several disadvantages including training instability, random seed sensitivity, and other optimization problems. In this work, we reframe VINs as recurrent-convolutional networks which demonstrates that VINs couple recurrent convolutions with an unconventional max-pooling activation. From this perspective, we argue that standard gated recurrent update equations could potentially alleviate the optimization issues plaguing VIN. The resulting architecture, which we call the Gated Path Planning Network, is shown to empirically outperform VIN on a variety of metrics such as learning speed, hyperparameter sensitivity, iteration count, and even generalization. Furthermore, we show that this performance gap is consistent across different maze transition types, maze sizes and even show success on a challenging 3D environment, where the planner is only provided with first-person RGB images. Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric P. Xing, Ruslan Salakhutdinov |
ICML | 1 |
| 2017 | Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute DetectionabstractComputers still struggle to understand the interdependency of objects in the scene as a whole, e.g., relations between objects or their attributes. Existing methods often ignore global context cues capturing the interactions among different object instances, and can only recognize a handful of types by exhaustively training individual detectors for all possible relationships. To capture such global interdependency, we propose a deep Variation-structured Re-inforcement Learning (VRL) framework to sequentially discover object relationships and attributes in the whole image. First, a directed semantic action graph is built using language priors to provide a rich and compact representation of semantic correlations between object categories, predicates, and attributes. Next, we use a variation-structured traversal over the action graph to construct a small, adaptive action set for each step based on the current state and historical actions. In particular, an ambiguity-aware object mining scheme is used to resolve semantic ambiguity among object categories that the object detector fails to distinguish. We then make sequential predictions using a deep RL framework, incorporating global context cues and semantic embeddings of previously extracted phrases in the state vector. Our experiments on the Visual Relationship Detection (VRD) dataset and the large-scale Visual Genome dataset validate the superiority of VRL, which can achieve significantly better detection results on datasets involving thousands of relationship and attribute types. We also demonstrate that VRL is able to predict unseen types embedded in our action graph by learning correlations on shared graph nodes. Xiaodan Liang, Lisa Lee, Eric P. Xing |
CVPR | 2 |
| 2017 | Dual Motion GAN for Future-Flow Embedded Video PredictionabstractFuture frame prediction in videos is a promising avenue for unsupervised video representation learning. Video frames are naturally generated by the inherent pixel flows from preceding frames based on the appearance and motion dynamics in the video. However, existing methods focus on directly hallucinating pixel values, resulting in blurry predictions. In this paper, we develop a dual motion Generative Adversarial Net (GAN) architecture, which learns to explicitly enforce future-frame predictions to be consistent with the pixel-wise flows in the video through a dual-learning mechanism. The primal future-frame prediction and dual future-flow prediction form a closed loop, generating informative feedback signals to each other for better video prediction. To make both synthesized future frames and flows indistinguishable from reality, a dual adversarial training method is proposed to ensure that the future-flow prediction is able to help infer realistic future-frames, while the future-frame prediction in turn leads to realistic optical flows. Our dual motion GAN also handles natural motion uncertainty in different pixel locations with a new probabilistic motion encoder, which is based on variational autoencoders. Extensive experiments demonstrate that the proposed dual motion GAN significantly outperforms state-of-the-art approaches on synthesizing new video frames and predicting future flows. Our model generalizes well across diverse visual scenes and shows superiority in unsupervised video representation learning. Xiaodan Liang, Lisa Lee, Wei Dai 0003, Eric P. Xing |
ICCV | 2 |
| 1990 | The effects of syntactic and discourse variables on the segmental intelligibility of speech
Judith C. Goodman, Howard C. Nusbaum, Lisa Lee, Kevin Broihier |
ICSLP | 3 |