Search Results - "Hejna, Joey"

Inverse Preference Learning: Preference-based RL without a Reward Function by Hejna, Joey, Sadigh, Dorsa

“…Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these…”
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Few-Shot Preference Learning for Human-in-the-Loop RL by Hejna, Joey, Sadigh, Dorsa

“…While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has…”
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Improving Long-Horizon Imitation Through Instruction Prediction by Hejna, Joey, Abbeel, Pieter, Pinto, Lerrel

“…Complex, long-horizon planning and its combinatorial nature pose steep challenges for learning-based agents. Difficulties in such settings are exacerbated in…”
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Distance Weighted Supervised Learning for Offline Interaction Data by Hejna, Joey, Gao, Jensen, Sadigh, Dorsa

“…Sequential decision making algorithms often struggle to leverage different sources of unstructured offline interaction data. Imitation learning (IL) methods…”
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MotIF: Motion Instruction Fine-tuning by Hwang, Minyoung, Hejna, Joey, Sadigh, Dorsa, Bisk, Yonatan

“…While success in many robotics tasks can be determined by only observing the final state and how it differs from the initial state - e.g., if an apple is…”
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Re-Mix: Optimizing Data Mixtures for Large Scale Imitation Learning by Hejna, Joey, Bhateja, Chethan, Jian, Yichen, Pertsch, Karl, Sadigh, Dorsa

“…Increasingly large imitation learning datasets are being collected with the goal of training foundation models for robotics. However, despite the fact that…”
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From $r$ to $Q^$: Your Language Model is Secretly a Q-Function by Rafailov, Rafael, Hejna, Joey, Park, Ryan, Finn, Chelsea

“…Reinforcement Learning From Human Feedback (RLHF) has been critical to the success of the latest generation of generative AI models. In response to the complex…”
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So You Think You Can Scale Up Autonomous Robot Data Collection? by Mirchandani, Suvir, Belkhale, Suneel, Hejna, Joey, Choi, Evelyn, Islam, Md Sazzad, Sadigh, Dorsa

“…A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the…”
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Extreme Q-Learning: MaxEnt RL without Entropy by Garg, Divyansh, Hejna, Joey, Geist, Matthieu, Ermon, Stefano

“…Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an…”
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Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms by Rafailov, Rafael, Chittepu, Yaswanth, Park, Ryan, Sikchi, Harshit, Hejna, Joey, Knox, Bradley, Finn, Chelsea, Niekum, Scott

“…Reinforcement Learning from Human Feedback (RLHF) has been crucial to the recent success of Large Language Models (LLMs), however, it is often a complex and…”
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Show, Don't Tell: Aligning Language Models with Demonstrated Feedback by Shaikh, Omar, Lam, Michelle, Hejna, Joey, Shao, Yijia, Bernstein, Michael, Yang, Diyi

“…Language models are aligned to emulate the collective voice of many, resulting in outputs that align with no one in particular. Steering LLMs away from generic…”
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Contrastive Preference Learning: Learning from Human Feedback without RL by Hejna, Joey, Rafailov, Rafael, Sikchi, Harshit, Finn, Chelsea, Niekum, Scott, Knox, W. Bradley, Sadigh, Dorsa

“…Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular paradigm for aligning models with human intent. Typically RLHF algorithms operate in…”
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Vision Language Models are In-Context Value Learners by Ma, Yecheng Jason, Hejna, Joey, Wahid, Ayzaan, Fu, Chuyuan, Shah, Dhruv, Liang, Jacky, Xu, Zhuo, Kirmani, Sean, Xu, Peng, Driess, Danny, Xiao, Ted, Tompson, Jonathan, Bastani, Osbert, Jayaraman, Dinesh, Yu, Wenhao, Zhang, Tingnan, Sadigh, Dorsa, Xia, Fei

“…Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress…”
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Octo: An Open-Source Generalist Robot Policy by Octo Model Team, Ghosh, Dibya, Walke, Homer, Pertsch, Karl, Black, Kevin, Mees, Oier, Dasari, Sudeep, Hejna, Joey, Kreiman, Tobias, Xu, Charles, Luo, Jianlan, Tan, You Liang, Chen, Lawrence Yunliang, Sanketi, Pannag, Vuong, Quan, Xiao, Ted, Sadigh, Dorsa, Finn, Chelsea, Levine, Sergey

“…Large policies pretrained on diverse robot datasets have the potential to transform robotic learning: instead of training new policies from scratch, such…”
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DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset by Khazatsky, Alexander, Pertsch, Karl, Nair, Suraj, Balakrishna, Ashwin, Dasari, Sudeep, Karamcheti, Siddharth, Nasiriany, Soroush, Srirama, Mohan Kumar, Chen, Lawrence Yunliang, Ellis, Kirsty, Fagan, Peter David, Hejna, Joey, Itkina, Masha, Lepert, Marion, Ma, Yecheng Jason, Miller, Patrick Tree, Wu, Jimmy, Belkhale, Suneel, Dass, Shivin, Ha, Huy, Jain, Arhan, Lee, Abraham, Lee, Youngwoon, Memmel, Marius, Park, Sungjae, Radosavovic, Ilija, Wang, Kaiyuan, Zhan, Albert, Black, Kevin, Chi, Cheng, Hatch, Kyle Beltran, Lin, Shan, Lu, Jingpei, Mercat, Jean, Rehman, Abdul, Sanketi, Pannag R, Sharma, Archit, Simpson, Cody, Vuong, Quan, Walke, Homer Rich, Wulfe, Blake, Xiao, Ted, Yang, Jonathan Heewon, Yavary, Arefeh, Zhao, Tony Z, Agia, Christopher, Baijal, Rohan, Castro, Mateo Guaman, Chen, Daphne, Chen, Qiuyu, Chung, Trinity, Drake, Jaimyn, Foster, Ethan Paul, Gao, Jensen, Herrera, David Antonio, Heo, Minho, Hsu, Kyle, Hu, Jiaheng, Jackson, Donovon, Le, Charlotte, Li, Yunshuang, Lin, Kevin, Lin, Roy, Ma, Zehan, Maddukuri, Abhiram, Mirchandani, Suvir, Morton, Daniel, Nguyen, Tony, O'Neill, Abigail, Scalise, Rosario, Seale, Derick, Son, Victor, Tian, Stephen, Tran, Emi, Wang, Andrew E, Wu, Yilin, Xie, Annie, Yang, Jingyun, Yin, Patrick, Zhang, Yunchu, Bastani, Osbert, Berseth, Glen, Bohg, Jeannette, Goldberg, Ken, Gupta, Abhinav, Gupta, Abhishek, Jayaraman, Dinesh, Lim, Joseph J, Malik, Jitendra, Martín-Martín, Roberto, Ramamoorthy, Subramanian, Sadigh, Dorsa, Song, Shuran, Wu, Jiajun, Yip, Michael C, Zhu, Yuke, Kollar, Thomas, Levine, Sergey, Finn, Chelsea

“…The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic…”
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