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Research teams at the RAI Institute had several papers accepted to IROS 2026, covering topics from closed- loop visuomotor policies and frameworks for self-supervised learning...
Presented at RSS 2026, Q2RL (Q-Estimation and Q-Gating from Behavior Cloning for Reinforcement Learning) is a method that avoids catastrophic forgetting when transitioning from offline...
Get a closer look at the Koala gripper! The co-designed system pairs a handheld data collector and a corresponding robotic gripper, allowing a single, unassisted...
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant...
Building upon the Sonata architecture, we introduce Vernata, consisting of three extensions: sparse view augmentation to improve robustness against varying point densities, a memory bank...
We propose that skills should jointly model action trajectories and the symbolic outcomes they induce. To address this gap, we introduce Predicate-Action Skills (PACTS), a...
How does a robot learn to recognize an object it’s never encountered? In this case, a demo can be worth a thousand words.
To truly understand how children envision a robot-integrated future, we used personal photo probes and a scaffolded design process to first uncover the values and...
Automatically converting demos into labeled datasets to enable the rapid training of task-specific object detectors that outperform existing state-of-the-art VLMs.
We propose two improvements to Residual RL that further enhance its sample efficiency and make it suitable for stochastic base policies. First, we leverage uncertainty...
We introduce real-is-sim, a new approach to integrating simulation into behavior cloning pipelines. In contrast to real-only methods, our framework allows policies to seamlessly switch...
We’ll be presenting robotic research that includes contact-rich bimanual manipulation and policy learning, an open source package for sample-based Model Predictive Control (MPC), how to...
