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Most assistive robots live in labs. We want to change that. FEAST enables care recipients to personalize mealtime assistance in-the-wild, with minimal researcher intervention across diverse in-home scenarios. 🏆 Outstanding Paper & Systems Paper Finalist Robotics: Science and Systems 🧵1/8

I know 2 min is forever for social media, but check out this video. Benjamin feeds himself a whole meal with FEAST---something he is not otherwise able to do. Very proud to be part of this project led by the legendary Rajat Kumar Jenamani

A wonderful project. The three takeaways are that home robots need adaptability, safety, and transparency to be deployed around people; if a machine is to be doing something as intimate as sharing your personal space at home and helping you eat, you need to be able to trust it,

Congrats Rajat Kumar Jenamani and the entire team EmPRISE Lab on this impressive accomplishment and being nominated for Best Paper Award and Best Systems Paper Award at #RSS 2025! This project took almost 2.5 years to get to this stage, and I am incredibly proud of what we have achieved

This week's #PaperILike is "Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments" (Stein et al., CoRL 2018). A highly original combination of learning + planning that is still underrated (despite winning a CoRL award!) PDF: proceedings.mlr.press/v87/stein18a/s…

🏆 Huge congratulations to the #RSS2025 Award Winners! roboticsconference.org/program/awards/

And we won the #RSS 2025 Best Paper Award! Congrats Rajat Kumar Jenamani and the entire EmPRISE Lab team Cornell Bowers Computing and Information Science 🎉🎉

Really excited to share that FEAST won the Best Paper Award at #RSS2025! Huge thanks to everyone who’s shaped this work, from roboticists to care recipients, caregivers, and occupational therapists. ❤️

This week's #PaperILike is "The Power of Resets in Online Reinforcement Learning" (Mhammedi et al., 2024). If you're doing RL in sim, why not use the sim to its full potential? Reset to any state! (gym.Env.reset() is not all we need.) PDF: arxiv.org/abs/2404.15417

This week's #PaperILike is "Effort Level Search in Infinite Completion Trees with Application to Task-and-Motion Planning" (Toussaint et al., ICRA 2024). Addresses the meta-reasoning challenge that is core to TAMP. Toussaint is always worth a read. PDF: user.tu-berlin.de/mtoussai/24-Co…

I'm teaching a new course this fall called "Robot Planning Meets Machine Learning." Half lectures, half seminar. Paper recommendations for the seminar part are welcome. I'm looking for any creative combinations of robot planning and ML.

This week's #PaperILike is "PushWorld: A benchmark for manipulation planning with tools and movable obstacles" (Kansky et al., 2023). Fans of benchmarks like ARC will enjoy the simple mechanics and the difficult reasoning required. PDF: arxiv.org/abs/2301.10289

LEAP is back! See you all at CoRL.

Hello world! This is Nishanth Kumar and I'll be taking over MIT CSAIL 's X/Twitter & Instagram for 24 hours! I'm a 4th year PhD MIT EECS working on AI/ML for Robotics and Computer Agents. Drop any and all questions about research, AI, MIT, or dogs (esp. robot dogs!) below 👇

How well are humans and AI at learning world models? We designed AutumnBench to measure exactly this, evaluating top reasoning models like #o3 alongside 517 humans. Read more about our findings and the benchmark release in the blog post.

Large robot datasets are crucial for training 🤖foundation models. Yet, we lack systematic understanding of what data matters. Introducing MimicLabs ✅System to generate large synthetic robot 🦾 datasets ✅Data-composition study 🗄️ on how to collect and use large datasets 🧵1/

This week's #PaperILike is "Stop! Planner Time: Metareasoning for Probabilistic Planning Using Learned Performance Profiles" (Budd et al., AAAI 2024). Metareasoning is increasingly important as we continue to make progress on "reasoning." PDF: ojs.aaai.org/index.php/AAAI…