.@julie_a_shah on the #futureofwork in @NYTmag: how can robots work with (not replace) people to make lives better? mobile.nytimes.com/2017/02/2…

.@MIT_CSAIL PhD candidate Felix Yanwei Wang is in the final year of his program working with the lab’s Interactive Robotics Group, researching robot learning, specifically inference-time policy alignment through human interactions. Read more about Felix: bit.ly/445dEtA

Want your robot to clean the kitchen your way? 🧹✨ 🔗yanweiw.github.io/itps/" Introducing Inference-Time Policy Steering: a training-free method that lets you specify where and how to manipulate objects, so you can guide non-interactive policies to align with your preferences!

Excited to present our #NeurIPS2024 Oral talk! 🚀 Enhancing Preference-based Linear Bandits via Human Response Time Coffee or tea? If you choose instantly, you likely have a strong preference. How can AI leverage this psychological insight to better learn human preferences? Curious? Don't think too long! Let's connect and explore how psychology drives smarter AI. 📅 Dec. 11, 3:30-3:50 PM PST 📍 Oral Session 2A: Agents (East Ballroom A, B) 👉 Conference Session neurips.cc/virtual/2024/post… 👉 Paper on arXiv arxiv.org/pdf/2409.05798

Announcing Versatile Demonstration Interface (VDI) – a tool for collaborative robots that makes it easier to collect task demonstrations using three common Learning from Demonstration approaches.

The work forces an agent to make decisions based on the current state's similarity to pre-defined human-interpretable prototypes. For example, here the state is similar to the prototypes for "Brake" and "Turn Left", so the agent brakes and turns left.

Talk to Serena at AAAI about how standard reward tuning via trial-and-error has many lurking dangers!

Neat work combining ideas from formal methods, stable control policies, and imitation learning!

Before everyone flees twitter... new paper coming out at NeurIPS! Humans compress meanings into complexity limited discrete representations (words). Can neural nets learn similar communication? Yes! (1/7)

Super grateful for this chance to continue exciting *interdisciplinary* research. Thanks to my advisor, @julie_a_shah, but also so many collaborators from other departments (@roger_p_levy and @NogaZaslavsky) and inspiring labmates and researchers.

New journal paper on Latent Space Alignment! Neural agents learn latent representations spaces, but often each agent learns its own idiosyncratic space. How can we align those space among agents or even with humans? tandfonline.com/doi/full/10.…

Happy to announce... Well, this paper didn't get in, but I still think it's neat. Using the same probe-based method for testing if language models use representations of syntax, we can "fix" RL agent perception (e.g., notice an oncoming car): arxiv.org/abs/2201.12938

It's Monday morning, which means it's the perfect time to start thinking about information theory, brains, and neural nets! Submit to #InfoCog2022 @NeurIPSConf Organized by @NogaZaslavsky @gershbrain @sepalmerNeuro @C4COMPUTATION sites.google.com/view/infoco…

📣 Very excited to announce our in-person #NeurIPS2022 workshop on Information-Theoretic Principles in Cognitive Systems! Check out our lineup of invited speakers and CFP, submit short papers by September 19 sites.google.com/view/infoco… #InfoCog2022 @NeurIPSConf

New paper (to appear in ICML)! Using a new prototype-based classifier, we show how notions of fair and hierarchical classification are tightly related, and how we can directly control "concept relationships" to switch between modes.

What a great start of @ieee_ras_icra. The #icra2022 workshop on #cobots and @workofthefuture organised by @robo_kween, @julie_a_shah, Chris Fourie, Ben Armstrong & co-organisers was wonderful! sites.google.com/view/icra22…