Jason Kim @jsonbot117
🔥#Legged_Robot_SLAM, State Estimation & Locomotion🔥Open Source Robotics / Now at URobotics, MS. EECS github.com/U-AMC Joined December 2016-
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Today's humanoid robots are beasts in controlled environments. You can see that in our posts. Send one humanoid out to buy groceries and it falls apart. It can't reliably climb your stairs, cross a cracked sidewalk, or hold a line going downhill. Most of that gap isn't the hardware. It's the control policy. These policies are reactive: the robot maps what it sees right now straight to an action. No model of what its own body is about to do next. ParkourFormer goes straight at this. The idea in plain terms: before acting, it predicts its own body state a couple of frames ahead. Then it picks the action based on that forecast. Reaction turns into anticipation. A small prediction head forecasts the next two proprioceptive states: joint angles, velocities, balance. Those predictions get fed directly into the action head. The robot moves based on where it expects to land next. Not just what's under it right now. How much does this matter? Remove the supervised future-prediction loss in training and descending-stairs success collapses from 95% to under 10%. When you can't see the ground under your next step, anticipation is the whole game. The headline numbers, on a Unitree G1 with 29 joints, across nine terrains: 93.85% average traversal success. Up to 42.73% above strong MLP, MoE, and vanilla Transformer baselines. The biggest margins show up on the hardest terrains. One unified policy for stairs, gaps, slopes, rough ground, and obstacles. No per-terrain tuning. The honest caveat: this is still staged terrain, and it leans hard on RGB-D depth. Cut the depth feed and the policy fails completely. So not grocery-run ready. But "predict your next body state, then act" is the kind of shift that actually narrows the lab-to-street gap.
🤷What if we want to learn from human data... without human data? In our work NIL (No-data Imitation Learning) @CVPR, we explore a simple but ambitious question: Can robots learn directly from AI-generated videos without any curated demonstration data? 🔗nil.is.tue.mpg.de
Holy crap atarilab.github.io/motiondisco.io/
AME2 has got two follow-ups which can significantly improve training efficiency, performance, and generalization. I think the training cost can be reduced to (not by) 20% now. 1) active gaze arxiv.org/abs/2606.05880 2) sparsify attention arxiv.org/abs/2606.00637
releasing AME2: Agile and Generalized Legged Locomotion via Attention-Based Neural Map Encoding arxiv.org/abs/2601.08485 In this work, we discuss how to achieve a combination of generalization and agility in legged locomotion, and propose a general solution.
Best Conference Paper Award and the Best Paper Award on Robot Manipulation and Locomotion at #ICRA2026!
High-quality motion reference data is key for humanoid skill learning 🤖🕺💃 A natural idea is to leverage human motions and “translate” them to humanoid motions, a process known as retargeting. For interaction-rich tasks such as scene interaction and loco-manipulation,
Nice new blog post by David Rosen on certifiable factor graph optimization in GTSAM: gtsam.org/2026/06/02/cer… Together with the chordal SDP work I posted about recently, this should soon lead to some very cool new certifiable optimization features in GTSAM!!!
#ICRA2026 paper "DroneKey++: A Size Prior-free Method and New Benchmark for Drone 3D Pose Estimation from Sequential Images" @bini_und, Yeong-Jun Cho abs: arxiv.org/abs/2602.06211
Performance Upgraded. Possibilities Expanded. Our DR02 humanoid robot continues to evolve with enhanced payload capacity and obstacle-crossing capabilities, unlocking greater potential for real-world industry applications !
Cool work led by @ctki49 @mxu_cg @KehanWen170077 collaboration between @leggedrobotics @xbpeng4 youtube.com/watch?v=OY61Ti…
wholebodylocomotion.github.io In this work we explored how to repurpose terrain-aware human locomotion skills in a scalable and versatile way: like the action-free world model, we predict how humans will move online, and then let the humanoid track it with RL.
「特徴の欠如」と「地形条件変化」に頑強な LiDAR-IMU-Leg Odometry を明日の ICRA2026 にて発表します。 @k_koide3 の元での、ラストの修行成果です。 takuokawara.github.io/RAL2025_projec… #robot
@k_koide3 さんのもとでのラストの修行成果がRA-Lに採択されました 特徴が乏しい環境（トンネルや月面）に頑強な位置推定を行うべく，「オドメトリ推定」と「レッグオドメトリの学習」を同時に解いています． paper: arxiv.org/pdf/2506.09548 project page: takuokawara.github.io/RAL2025_projec… #宇宙ロボット
Equivariant Filter for Radar-Inertial Odometry Giulio Delama, Jan Michalczyk, Morten Nissov, Martin Scheiber, Alessandro Fornasier, Kostas Alexis, Stephan Weiss arxiv.org/abs/2604.23033 [𝚌𝚜.𝚁𝙾]
We talk a lot about how deep learning can enable new robot capabilities but a lot less about how we can use it to make robot capabilities more resilient and therefore make robots more deployable Slide from an interesting talk by Prof Kostas Alexis in robots in the wild workshop at icra
아구밍 아커만 @aguming_
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이대은(Dae-eun Lee... @2daeeun
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LarissaCloud🕯 @larissacloud__
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heyyyy @negativering
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Bart Rosier @bartjrosier
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xiaoshi @xiaoshitree
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PM @omarcominyo11
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バンビー@Crescent... @bambi_rcj
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Erdil Karpat @HeissenBergUnc_
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deznuts_ @ganglimmm
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Daniel Truong @DanielTruong999
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Well Soar @SoarWell
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ロボ太郎 @robotaroelec
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Alex Song @AlexSong41595
21 Followers 371 Following 不要当中共的奴隶。les，LGBT pride🌈🌈🌈🌈。永远不要放弃希望，坚持下去早晚会有出路的。
Daniel Palenicek @DPalenicek
403 Followers 222 Following Intern at @Meta, PhD Researcher in Robot #ReinforcementLearning 🤖🧠 at @ias_tudarmstadt and @Hessian_AI advised @Jan_R_Peters. Prev: @BoschGlobal, @Huawei
Space and Technology @spaceandtech_
30K Followers 381 Following Unveiling the wonders of the cosmos, breakthroughs in innovations. 📧 [email protected]
Jacob Van Meter @jacobvm04
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ソースカツ調査... @chousashikun
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Steven Cheng @stevencheng
3K Followers 7K Following Working on multiple vision-based open-source robot prototypes. #AI #Robotics #Nenpower #NBA #Cambridge
s t @stoai123
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Salih Yalcin @yalcin_salihh
865 Followers 5K Following Ph.D. Student in Comp. Eng. | Co-Founder @lieva_tr | Biomedical Engineer & Electrical-Electronics Engineer | Lecturer @AbdullahGul_UNV
아구밍 아커만 @aguming_
2K Followers 977 Following 프로그래밍 + 일상계/ 기니피그를 사랑하는 모임 명예 회원 / 코덱스 마니아 /https://t.co/oIAzy4gYIB https://t.co/yTfua7EWVR
바보버스 @minimanimoxx
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LarissaCloud🕯 @larissacloud__
652 Followers 1K Following aka. 라리사. 관심사,RT,말 많아요. 무례함,차별 싫어하고 혐오자 안 사요. 소속의 의견과 무관합니다. 알티 != 동의 . 트윗 내용 불펌 금지 마스토톤: https://t.co/6vK8t7pVG6 블스: @cetaceanightsky.bsky.social
Ryan @ryankwondev
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Fin @Fin__nn
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Revant Mahajan @MahajanRevant
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Jacob Van Meter @jacobvm04
74 Followers 848 Following robotics/ai research | unc dropout | prev @plasticlabs
YUJI | AI Robotics @yuji_fujima
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Inbok @inboklee
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Youngsun Wi @WiYoungsun
484 Followers 199 Following Research Scientist at @GoogleDeepMind Gemini Robotics 🦾🤖| @UMRobotics Alumni | Interned at @AIatMeta @NVIDIARobotics @amazondrives
ろーてんてい @loutlot
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BoosterTroubler @BoosterTroubler
121 Followers 1K Following International Partnerships @boosterobotics Connecting humanoid robotics with the world. All opinions are mine.
ロボ犬 研究所@T... @techdrone_lab
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Lokesh Krishna @lok_s_h
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stephen @_ildsjel
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45 Followers 1K Following MSc student in Intelligent Robotics, Robotics Innovatory Laboratory, Sungkyunkwan University
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3K Followers 7K Following Working on multiple vision-based open-source robot prototypes. #AI #Robotics #Nenpower #NBA #Cambridge
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Alvaro L @L42ARO
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pepper shelter @saltygirlpepper
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Bruno Santos🇵🇹 @brunoeducsant
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소수자 G🏔️ @dev_mizodd
179 Followers 792 Following 데이터 잡부개발자⚙️ 매일 죽음을 준비하는 마음으로 최선을 다해서 사는 사람 🌈 다들 자신의 삶에서 최고 멋쟁이로 사십시오 😎
👨🎤 ʐɨɢɢ�... @z4ziggy
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765 Followers 164 Following Professor of Computer Science @ Oregon State University. AI and Robotics research emphasis on learning/planning for humanoid robot and AI for Agriculture.
Junfan Zhu 朱俊帆 ... @junfanzhu98
3K Followers 4K Following AI Research, 🤖+World Model @saturdayrobotic | @UChicago Math, @GeorgiaTech CS, @StanfordGSB, ex-Quant📈 | Wuxi🇨🇳✈️World 60+countries🗺️🌎🍷🎻🏞️🚁🔫⛵
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