Mark Van der Merwe

31 posts

Mark Van der Merwe

Mark Van der Merwe

@MarkVanderMerwe

PhD @UMRobotics | Previously: Intern @NASAJPL, Intern @Mila_Quebec, Undergraduate @UUtah Computer Vision, Robotics, and Machine/Deep Learning.

参加日 Ekim 2019
284 フォロー中142 フォロワー
Mark Van der Merwe がリツイート
Youngsun Wi
Youngsun Wi@WiYoungsun·
Dexterous hands vary widely—so do tactile modalities. 🖐️🌈 Our vision on tactile human-to-robot transfer: 🔓 Not tied to specific hardware ♻️ Reuse human tactile demos across embodiments Presenting TactAlign, a cross-sensor tactile alignment for cross-embodiment policy transfer.
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Michigan Robotics
Michigan Robotics@UMRobotics·
Earlier this year, @NimaFazeli7 recieved a @NSF CAREER Award. We took the chance to catch up with Professor Fazeli to learn a bit more about the fascinating research on "intelligent and dexterous robots that seamlessly integrate vision and touch.”
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Luís Marques
Luís Marques@TheLuisMarques·
Is dynamics model mismatch breaking your robotic safety guarantees? We used Conformal Prediction to construct probabilistically safe trajectories given approximate Gaussian dynamics models. Learn more at um-arm-lab.github.io/lucca Presented @wafr_conf & @michigan_AI's Symposium
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Boyang Wang
Boyang Wang@BoyangWang7·
Our This&That: Language-Gesture Controlled Video Generation for Robot Planning testing code of Video Diffusion Model is released at github: github.com/Kiteretsu77/Th… The rest will be coming soon (working hard to organizing the code😆)
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Boyang Wang
Boyang Wang@BoyangWang7·
Verry happy to share our new paper, This&That, an dynamic robot video generation model with language and simple gestures conditioning! Moreover, we also propose Diffusion Video to Action (DiVA) model to transfer generated videos to robot actions in the rollout environment.
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Jeong Joon Park
Jeong Joon Park@jjpark3D·
Imagine controlling robots with simple gestures! We've developed a system that lets you point at objects and tell robots to 'move this' or 'close that,' with language-gesture-controlled video generation! Check out our project dubbed "this&that": cfeng16.github.io/this-and-that/
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Zixuan Huang
Zixuan Huang@ZixuanHuang15·
MPC is powerful but doesn’t work well for long-horizon tasks without reference trajectories, which are usually expensive to compute online Introducing Subgoal Diffuser to generate subgoals at appropriate temporal resolution dynamically to guide MPC sites.google.com/view/subgoal-d… 🧵↓
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Peter Mitrano
Peter Mitrano@PeterMitrano·
I successfully defended my PhD! Thank you to my advisor Dmitry Berenson, and my lab mates in the @umicharmlab ! 🎓🔬🤖 If you're interested in hiring me to work on learning & planning for robotic manipulation, send me a message!
Peter Mitrano tweet mediaPeter Mitrano tweet media
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Youngsun Wi
Youngsun Wi@WiYoungsun·
How can a 🤖 plan and control tools (e.g., 🧽 🧹) for contact-rich tasks given visual-language inputs? CALAMARI 🦑 shows how we can handle this problem in a very generalizable and data-efficient way via a spatial-action map representation! (1/n) @corl_conf
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Nima Fazeli
Nima Fazeli@NimaFazeli7·
Robots can slide objects on flat surfaces when they are too big/heavy to lift. But what if they cannot push on the side of the object? Xili Yi shows how robots can use top contact to certifiably push the object to any desired configuration #RSS2023 arxiv.org/abs/2305.14289
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Nima Fazeli
Nima Fazeli@NimaFazeli7·
Close your eyes and pick up two objects, one in each hand. Can you guess their poses just from poking them against each other? Our robots can! Check out MultisSCOPE, Andrea Sipos' paper accepted at RSS 2023: arxiv.org/abs/2305.14204 #RSS2023 @UMRobotics @UMengineering
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Peter Mitrano
Peter Mitrano@PeterMitrano·
I'll be presenting our paper on FOCUS tomorrow in the morning #ICRA2023 Come see how a clever tweak to the fine tuning process can improve data efficiency for sim2real transfer!
Peter Mitrano tweet media
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