Kushal
@kushalk_
PhD student @PortalCornell | Interested in Robot Learning, Human-Robot Collaboration, and Motion Planning | UG @IITKgp
Sharing a few more demos of our first #LargeBehaviorModel (LBM) at TRI. 1/🍎This model enables a robot to learned to core and cut an apple into multiple slices autonomously. We trained our diffusion-based LBM on almost 1,700 hours of robot data, conducted 1,800 real-world…
Using OT to define rewards for imitating video demos is popular, but it breaks down when demos are temporally misaligned—a frequent challenge in practice. We present ORCA at #ICML2025 , which defines rewards by aligning sequences, rather than matching individual frames via OT.
🔍 How can we build AI agents that reason about the physical world the way humans do (or better) ? Excited to share Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel, which will be presented next Thursday July 17th at ICML in Vancouver! 👇(1/6)
How can 🤖 learn from human workers to provably reduce their workload in factories? Our latest @RoboticsSciSys paper answers this question by proposing the first cost-optimal interactive learning (COIL) algorithm for multi-task collaboration.
X-Sim, best Paper (Runner-Up) at the EgoAct Workshop @ RSS. Real2sim2real with pure image based simulation training and reward extracted from human videos
Teleoperation is slow, expensive, and difficult to scale. So how can we train our robots instead? Introducing X-Sim: a real-to-sim-to-real framework that trains image-based policies 1) learned entirely in simulation 2) using rewards from human videos. portal-cornell.github.io/X-Sim
So you’ve trained your favorite diffusion/flow based policy, but it’s just not good enough 0-shot. Worry not, in our new work DSRL - we show how to *steer* pre-trained diffusion policies with off-policy RL, improving behavior efficiently enough for direct training in the real…
Diffusion policies have demonstrated impressive performance in robot control, yet are difficult to improve online when 0-shot performance isn’t enough. To address this challenge, we introduce DSRL: Diffusion Steering via Reinforcement Learning. (1/n) diffusion-steering.github.io
Interactable Digital Twins hold great promises. It allows us to train in sim and test in real. But can we go a step further? Can we deploy a robot w/o training? Key idea: simulate the outcome of each action with Digital Twins and use VLM as critic to select the best action.
How can robots solve tasks that demand both semantic and physical reasoning, like playing real-world Angry Birds, without tons of data? We introduce Prompting with the Future: an MPC framework that fuses a pretrained VLM with an interactive digital twin for grounded, open-world…
Just a small reminder that our workshop is happening tomorrow, and we have an amazing line of speakers! Make sure to check out the workshop website for the schedule. 🤖
📢Exciting news! Our workshop Human-in-the-Loop Robot Learning: Teaching, Correcting, and Adapting has been accepted to RSS 2025!🤖🎉Join us as we explore how robots can learn from and adapt to human interactions and feedback. 🔗Workshop website: hitl-robot-learning.github.io 🧵👇
It was a dream come true to teach the course I wish existed at the start of my PhD. We built up the algorithmic foundations of modern-day RL, imitation learning, and RLHF, going deeper than the usual "grab bag of tricks". All 25 lectures + 150 pages of notes are now public! 🧵
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 @RoboticsSciSys 🧵1/8
Searching within a photorealistic digital twin is a cool idea to solve general purpose tasks!
How can robots solve tasks that demand both semantic and physical reasoning, like playing real-world Angry Birds, without tons of data? We introduce Prompting with the Future: an MPC framework that fuses a pretrained VLM with an interactive digital twin for grounded, open-world…