new papers

USC-Lira · Sep 1, 2024 · ff375b3 · ff375b3
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                                     <li>Myers et al., <a href="https://arxiv.org/pdf/2302.13507.pdf">Active Reward Learning from Online Preferences</a> (2023).</li>
 									<li>Bobu et al., <a href="https://arxiv.org/pdf/2201.07082.pdf">Inducing Structure in Reward Learning by Learning Features</a> (2022).</li>
                                     <li>Hadfield-Menell et al., <a href="https://arxiv.org/pdf/1711.02827.pdf">Inverse Reward Design</a> (2017).</li>
+									<li>Rafailov et al., <a href="https://arxiv.org/pdf/2305.18290">Direct Preference Optimization: Your Language Model is Secretly a Reward Model</a> (2024).</li>
                                     <li>Kwon et al., <a href="https://arxiv.org/pdf/2001.04377.pdf">When Humans aren't Optimal: Robots that Collaborate with Risk-aware Humans</a> (2020).</li>
 									<li>Awad et al., <a href="https://www.academia.edu/download/61035399/2018_Article_The_Moral_Machine20191027-99372-l8c1xh.pdf">The Moral Machine Experiment</a> (2018).</li>
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@@ -330,6 +331,8 @@ <h3 id="topics">Timeline</h3>
                                     <li>Sharma et al., <a href="https://arxiv.org/pdf/2204.05186.pdf">Correcting Robot Plans with Natural Language Feedback</a> (2022).</li>
                                     <li>Singh et al., <a href="https://arxiv.org/pdf/2209.11302.pdf">ProgPrompt: Generating Situated Robot Task Plans using Large Language Models</a> (2023).</li>
                                     <li>Brohan et al., <a href="https://arxiv.org/pdf/2307.15818.pdf">RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control</a> (2023).</li>
+									<li>Kim et al., <a href="https://arxiv.org/pdf/2406.09246">OpenVLA: An Open-Source Vision-Language-Action Model</a> (2024).</li>
+									<li>Duan et al., <a href="https://arxiv.org/pdf/2406.18915">Manipulate-Anything: Automating Real-World Robots using Vision-Language Models</a> (2024).</li>
                                 </ul>
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