16 citations · 45 across the 18 of their papers we have counts for
15 papers · 1 filter
Adaptive Model Predictive Control by Learning Classifiers
Rel Guzman, Rafael Oliveira, Fabio Ramos
Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the pres…
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
Eric Heiden, Miles Macklin, Yashraj Narang +3
Robotic cutting of soft materials is critical for applications such as food processing, household automation, and surgical manipulation. As in other areas of robotics, simulators c…
Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Estimation of a dynamical system's latent state subject to sensor noise and model inaccuracies remains a critical yet difficult problem in robotics. While Kalman filters provide th…
Parallelised Diffeomorphic Sampling-based Motion Planning
Tin Lai, Weiming Zhi, Tucker Hermans +1
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP). PDMP is a novel parallelised framework that uses bijective and differentiable mappings, or diffeomorphi…
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
Rika Antonova, Fabio Ramos, Rafael Possas +1
BayesSim is a statistical technique for domain randomization in reinforcement learning based on likelihood-free inference of simulation parameters. This paper outlines BayesSimIG:…
Probabilistic Trajectory Prediction with Structural Constraints
Weiming Zhi, Lionel Ott, Fabio Ramos
This work addresses the problem of predicting the motion trajectories of dynamic objects in the environment. Recent advances in predicting motion patterns often rely on machine lea…