HOME / POSTS / ROBOTIC MANIPULATION READING GROUP
Robotic Manipulation Reading Group
June 2022 Bianchini
This is the official paper archive of the Manipulation Reading Group started by members of Penn's DAIR Lab. The group meets weekly to go over papers devoted to robotic manipulation. Everyone is welcome. At the time of updating this post, our meetings occur in person, though feel free to reach out to me via email if you are not in the Philadelphia area and would like to request a virtual participation option.
- March 20: Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation, by Wenyan Yang, Alexandre Angleraud, Roel S. Pieters, Joni Pajarinen, Joni-Kristian Kämäräinen (2023).
- March 13: Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation, by I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert, and Youngwoon Lee (2021).
- March 6: Learning the Dynamics of Compliant Tool-Environment Interaction for Visuo-Tactile Contact Servoing, by Mark Van der Merwe, Dmitry Berenson, and Nima Fazeli (2022).
- February 27: AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer, by Allen Z. Ren, Hongkai Dai, Benjamin Burchfiel, and Anirudha Majumdar (2023).
- December 16: Neural Contact Fields: Tracking extrinsic contact with tactile sensing, by Carolina Higuera, Siyuan Dong, Byron Boots, and Mustafa Mukadam (2022).
- December 2: SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion, by Julen Urain, Niklas Funk, Jan Peters, and Georgia Chalvatzaki (2022).
- November 18: OSCAR: Data-driven operational space control for adaptive and robust robot manipulation, by Josiah Wong, Viktor Makoviychuk, Anima Anandkumar, and Yuke Zhu (2022).
- November 11: RoCUS: Robot Controller Understanding via Sampling, by Yilun Zhou, Serena Booth, Nadia Figueroa, and Julie Shah (2022).
- November 4: Torque-limited manipulation planning through contact by interleaving graph search and trajectory optimization, by Ramkumar Natarajan, Garrison L.H. Johnston, Nabil Simaan, Maxim Likhachev, and Howie Choset (2022).
- October 28: Choosing poses for force and stiffness control, by Arash Ajoudani, Nikos G. Tsagarakis, and Antonio Bicchi (2017).
- October 21: Manipulation via membranes: high-resolution and highly deformable tactile sensing and control, by Miquel Oller, Mireia Planas, Dmitry Berenson, and Nima Fazeli (2022).
- October 14: Human-to-robot imitation in the wild, by Shikhar Bahl, Abhinav Gupta, and Deepak Pathak (2022).
- October 7: VIRDO++: Real-world, visuo-tactile dynamics and perception of deformable objects, by Youngsun Wi, Andy Zeng, Pete Florence, and Nima Fazeli (2022).
- September 30: Masked imitation learning: discovering environment-invariant modalities in multimodal demonstrations, by Yilun Hao, Ruinan Wang, Zhangjie Cao, Zihan Wang, Yuchen Cui, and Dorsa Sadigh (2022).
- August 17: Residual reinforcement learning for robot control, by Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, and Sergey Levine (2019).
- August 10: Learning multi-object dynamics with compositional neural radiance fields, by Danny Driess, Zhiao Huang, Yunzhu Li, Russ Tedrake, and Marc Toussaint (2022).
- August 3: R3M: A universal visual representation for robot manipulation, by Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta (2022).
- July 27: 3D neural scene representations for visuomotor control, by Yunzhu Li, Shuang Li, Vincent Sitzmann, Pulkit Agrawal, and Antonio Torralba (2022).
- July 20: Manipulation of unknown objects via contact configuration regulation, by Neel Doshi, Orion Taylor, and Alberto Rodriguez (2022).
- July 13: Collaborative manipulation of spherical-shape objects with a deformable sheet held by a mobile robotic team, by Kyle Hunte and Jingang Yi (2021).
- July 6: Data augmentation for manipulation, by Peter Mitrano and Dmitry Berenson (2022).
- June 29: Global planning for contact-rich manipulation via local smoothing of quasi-dynamic contact models, by Tao Pang, H.J. Terry Suh, Lujie Yang, and Russ Tedrake (2022).