103 citations · 192 across the 31 of their papers we have counts for
6 papers · 1 filter
Probabilistic Differentiable Filters Enable Ubiquitous Robot Control with Smartwatches
Fabian C Weigend, Xiao Liu, Heni Ben Amor
Ubiquitous robot control and human-robot collaboration using smart devices poses a challenging problem primarily due to strict accuracy requirements and sparse information. This pa…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Enhancing State Estimation in Robots: A Data-Driven Approach with Differentiable Ensemble Kalman Filters
Xiao Liu, Geoffrey Clark, Joseph Campbell +2
This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble Kalma…
Learning Soft Robot Dynamics using Differentiable Kalman Filters and Spatio-Temporal Embeddings
Xiao Liu, Shuhei Ikemoto, Yuhei Yoshimitsu +1
This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training t…
Certifiably-correct Control Policies for Safe Learning and Adaptation in Assistive Robotics
Keyvan Majd, Geoffrey Clark, Tanmay Khandait +4
Guaranteeing safety in human-centric applications is critical in robot learning as the learned policies may demonstrate unsafe behaviors in formerly unseen scenarios. We present a…
Safe Robot Learning in Assistive Devices through Neural Network Repair
Keyvan Majd, Geoffrey Clark, Tanmay Khandait +4
Assistive robotic devices are a particularly promising field of application for neural networks (NN) due to the need for personalization and hard-to-model human-machine interaction…