28 citations · 92 across the 41 of their papers we have counts for
7 papers · 1 filter
Imitation and Supervised Learning of Compliance for Robotic Assembly
Devesh K. Jha, Diego Romeres, William Yerazunis +1
We present the design of a learning-based compliance controller for assembly operations for industrial robots. We propose a solution within the general setting of learning from dem…
Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units
Shiyu Jin, Diego Romeres, Arvind Ragunathan +2
This paper presents a novel trajectory optimization formulation to solve the robotic assembly of the belt drive unit. Robotic manipulations involving contacts and deformable object…
PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance
Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres
PYROBOCOP is a lightweight Python-based package for control and optimization of robotic systems described by nonlinear Differential Algebraic Equations (DAEs). In particular, the p…
Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
Alberto Dalla Libera, Fabio Amadio, Daniel Nikovski +2
In this paper, we consider the use of black-box Gaussian process (GP) models for trajectory tracking control based on feedback linearization, in the context of mechanical systems.…
Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
Siyuan Dong, Devesh K. Jha, Diego Romeres +3
Object insertion is a classic contact-rich manipulation task. The task remains challenging, especially when considering general objects of unknown geometry, which significantly lim…
Model-based Policy Search for Partially Measurable Systems
Fabio Amadio, Alberto Dalla Libera, Ruggero Carli +2
In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i.e., systems where the state can not be directly measured,…
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
Fabio Amadio, Alberto Dalla Libera, Riccardo Antonello +3
In this paper, we present a Model-Based Reinforcement Learning (MBRL) algorithm named \emph{Monte Carlo Probabilistic Inference for Learning COntrol} (MC-PILCO). The algorithm reli…