Hanjiang Hu
46 posts
@huhanjiang
PhD candidate @CMU_ECE, @ICL_at_CMU, @CMU_Robotics | alum @mldcmu @cmu_SCS @sjtu1896 | Safety and robustness in ML, control, robotics. Opinions are my own
Can task structure emerge directly from robot demonstrations? We introduce ENAP: a bi-level neuro-symbolic policy that learns an interpretable automaton from visuomotor trajectories and uses it to guide continuous control. Instead of treating long-horizon manipulation as a pure black box, ENAP recovers task phases, branching behaviors, and recovery loops directly from data. Project page: intelligent-control-lab.github.io/ENAP-project-w… Paper: arxiv.org/abs/2603.25903
🚦Track 3 – Sensor Placement What’s the best way to place your LiDARs? #RoboSense2025 introduces a novel challenge: optimizing sensor placement to enhance 3D perception under cost and coverage constraints. 📝 Register: robosense2025.github.io 📦 Toolkit: github.com/robosense2025/…
🎉 Excited to announce 𝗧𝗵𝗲 𝗥𝗼𝗯𝗼𝗦𝗲𝗻𝘀𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 @IROS2025! 🌐 𝗪𝗲𝗯𝘀𝗶𝘁𝗲: robosense2025.github.io 📩 𝗖𝗼𝗻𝘁𝗮𝗰𝘁: robosense2025@gmail.com 🚀 Join us to tackle cutting-edge challenges in robust perception — with $10,000 cash prizes awaiting top teams!
There’s a shocking amount of useless rebranding in AI Safety. E.g., I have trouble understanding how “AI Control” is anything other than a new word for oversight and deployment monitoring — which has been a part of the broader agenda for a while.
I’ve always championed AI robustness over strict AI alignment. Robustness means building systems that can handle unexpected behaviors—adapting quickly without stifling innovation. (1/8) Read more: adaptiveagents.org/robustness
Introducing one of the fastest and safest robot controllers, for operational space and hierarchical tasks Deploy your learned policies, or teleoperate your robot confidently with OSCBF Website: stanfordasl.github.io/oscbf/ Preprint: arxiv.org/pdf/2503.06736 With @drmapavone
🚀 Excited to share our #ICLR2025 work on planning with neural dynamics models! While our lab has developed diverse neural dynamics models for manipulating rigid, deformable, and granular objects, having the model alone doesn’t solve the problem—planning with it remains a challenge. 💡 Enter BaB-ND, led by @Keyi_Shen_ and Jiangwei! We propose a scalable, GPU-accelerated branch-and-bound algorithm, inspired by neural network verification, to enable effective planning for diverse objects modeled with neural dynamics. 🔗 Project page (open-source + detailed docs!): robopil.github.io/bab-nd/ 🎥 Watch the video to see T being pushed around obstacles, and check out Keyi’s thread for more details!
Is Your LiDAR Placement Optimized for 3D Scene Understanding? #NeurIPS2024 Spotlight - Paper: arxiv.org/abs/2403.17009 - Code: github.com/ywyeli/Place3D We present Place3D, a full-cycle pipeline for LiDAR placement optimization, data generation, and downstream evaluations.
[1/4] 🌟Sneak Peek: SPARK in Action! 🦾 Previewing Safe Protective & Assistive Robot Kit (SPARK)—a modular toolbox designed to enhance safety in humanoid autonomy and teleoperation. Safety isn't just a feature—it's the foundation for humanoids to truly integrate into human life. SPARK filters risky actions, ensuring humanoids can achieve their objectives securely across tasks—from lab experiments to real-world deployments. With SPARK, you can innovate fearlessly, knowing safety is always guaranteed. Powered by Safe Set Algorithm (SSA), SPARK is built to: ✅ Configure safety behaviors with ease ⚖️ Balance safety and performance 🤖 Integrate with Unitree G1 + Apple Vision Pro 🔧 Support customization for other systems Stay tuned for the full release in a few weeks 🚀 Please see our website for the paper and more details! 🌐 Website: intelligent-control-lab.github.io/spark/ @ICL_at_CMU @CMU_Robotics @CarnegieMellon @UnitreeRobotics #Robotics #HumanoidSafety #AIInnovation
