Aditya Shirwatkar

Visiting Researcher @TUM_MIRMI,
PhD student @iiscbangalore,
@VNITN 2022.
I like to optimally control dynamical systems.

[C. Zhang @ChongZzZhang]

Holy crap

https://t.co/Uq2jGHnxkS

[Akshay Kulkarni @ak70000]

Excited to share that our paper on Concept Bottleneck Sparse Autoencoders (CB-SAE) will be presented at CVPR 2026 tomorrow!

CB-SAE combines sparse autoencoders (SAEs) with concept bottlenecks to make multimodal LLMs more interpretable and steerable.

[Aman Singh @singhaman1750]

1/5 Excited to announce that my paper “COMPAct: Computational Optimization and Automated Modular Design of Planetary Actuators” has been accepted at the #ICRA2026 🎉🤖
📄 Pre-print: https://t.co/jRl8WqAqOX
📂 Code: https://t.co/y0DGtQmqzd
🎥 Video: https://t.co/AM24cTE5fF

[Junzhe (JJ) He @JayHe748646]

Using attention-based map encoding, we enable robots to traverse extremely sparse terrains. We are now developing a new architecture that explicitly plans contact points, rather than latent representations, enabling whole-body contact planning for humanoid robots. Stay tuned.

[Rishika Bhagwatkar @rishika2110]

Excited to share that I'll be at #NeurIPS2025 from Dec 2-7, presenting two of my works 🚀

1. LLM Agent Safety Against Prompt Injections
2. Anomaly Detection Using VLMs

I’m looking for PhD positions starting Fall 2026.

Happy to connect, feel free to DM :)

Poster details in 🧵

[Rishika Bhagwatkar @rishika2110]

🚨 New Paper Alert!

🚀 Excited to introduce CAVE: A benchmark for detecting and explaining commonsense anomalies in real-world visual environments!

Accepted at #EMNLP2025 Main Conference! 🎉

📍Join us at our poster on Nov 5, 16:30-18:00 in Hall C.

[Jason Peng @xbpeng4]

Implementing motion imitation methods involves lots of nuisances. Not many codebases get all the details right. So, we're excited to release MimicKit!
https://t.co/7enUVUkc3h
A framework with high quality implementations of our methods: DeepMimic, AMP, ASE, ADD, and more to come!

[Lester Li @sizhe_lester_li]

Now in Nature! 🚀 Our method learns a controllable 3D model of any robot from vision, enabling single-camera closed-loop control at test time! This includes robots previously uncontrollable, soft, and bio-inspired, potentially lowering the barrier of entry to automation!

Paper: https://t.co/43qHC9sxZI

(1/n)

[Preston Culbertson @pdculbert]

🥋 We're excited to share judo: a hackable toolbox for sampling-based MPC (SMPC), data collection, and more, designed to make it easier to experiment with high-performance control.
Try it: pip install judo-rai

[Seohong Park @seohong_park]

Q-learning is not yet scalable

https://t.co/hoYUdAAeGZ

I wrote a blog post about my thoughts on scalable RL algorithms.

To be clear, I'm still highly optimistic about off-policy RL and Q-learning! I just think we haven't found the right solution yet (the post discusses why).

[Akshay Kulkarni @ak70000]

⚡Interested in making pretrained generative models interpretable with minimal training and annotations?

I'll be presenting our paper, Interpretable Generative Models through Post-hoc Concept Bottlenecks, at #CVPR2025 today in Poster Session 2 (4 pm - 6 pm CDT) at poster #266!

[Aditya Shirwatkar @hubble_02]

17/n If you’ve made it here, thanks a lot for your precious time🤗. Also, a big thanks to all of my co-authors (Naman Saxena, Kishore Chandra, Shishir N Y)

[Aditya Shirwatkar @hubble_02]

A 🧵 on our recent work at #ICRA25.

PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion (https://t.co/RuwznzrG3c)

[Aditya Shirwatkar @hubble_02]

16/n We’re actively extending our approach to overcome some of these limitations. Stay tuned! 😇