7 papers
RoboMonitor: Label-Efficient Runtime Monitoring of Robot Task Execution via Predictive Representation Learning
Abhiroop Ajith, Gokul Narayanan, Kyle Coelho +7
Learned robot policies produce actions, but their outputs alone do not establish whether execution is progressing as intended. Robot execution monitoring requires identifying the c…
Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
Brian Zhu, Momen Khalil, E Harrison +17
While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses…
A Deployment Case Study in Robotic Apparel Automation: Digital Twin Integration, Interoperability, and Workforce Enablement
Gokul Narayanan, Abhiroop Ajith, Jonathan Zornow +5
Despite steady advances in flexible automation in sectors such as electronics and automotive manufacturing, apparel automation remains challenging because fabrics are deformable an…
A Factory-Floor Deployment Case Study of VLA Pipelines for Industrial Packaging Task: Workflow, Failures, and Lessons
Brian Zhu, Philipp Schmitt, Philine Meister +12
Vision-Language-Action (VLA) policies have shown promising manipulation capabilities, yet their practical impact is often limited by the reliability demands of real-world deploymen…
Closing the Loop in Teleoperation: Episode-Level Data Quality Assessment and Feedback for High-Quality Demonstration Collection
Gokul Narayanan, Yash Shahapurkar, Melih Erdogan +2
Industrial automation is at a pivotal moment, as Physical AI is driving a transition from rigid, hand-engineered automation systems toward more flexible and adaptive systems. This…
Robotic Automation in Apparel Manufacturing: A Novel Approach to Fabric Handling and Sewing
Abhiroop Ajith, Gokul Narayanan, Jonathan Zornow +5
Sewing garments using robots has consistently posed a research challenge due to the inherent complexities in fabric manipulation. In this paper, we introduce an intelligent robotic…
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision
Ashvin Nair, Brian Zhu, Gokul Narayanan +2
Learning-based methods in robotics hold the promise of generalization, but what can be done if a learned policy does not generalize to a new situation? In principle, if an agent ca…