arXiv subjects

Antonio Paolillo

Publications and source records attributed to Antonio Paolillo.

24 records · Page 2Linked to original sources

arXiv2022

Learning Stable Dynamical Systems for Visual Servoing

This work presents the dual benefit of integrating imitation learning techniques, based on the dynamical systems formalism, with the visual servoing paradigm. On the one hand, dynamical systems allow to program additional skills without explicitly coding them in the visual servoing law, but leveraging few demonstrations of the full desired behavior. On the other, visual servoing allows to consider exteroception into the dynamical system architecture and be able to adapt to unexpected environment changes. The beneficial combination of the two concepts is proven by applying three existing dynamical systems methods to the visual servoing case. Simulations validate and compare the methods; experiments with a robot manipulator show the validity of the approach in a real-world scenario.

cs.RO↗

arXiv2021

Uncertainty-Aware Self-Supervised Learning of Spatial Perception Tasks

We propose a general self-supervised learning approach for spatial perception tasks, such as estimating the pose of an object relative to the robot, from onboard sensor readings. The model is learned from training episodes, by relying on: a continuous state estimate, possibly inaccurate and affected by odometry drift; and a detector, that sporadically provides supervision about the target pose. We demonstrate the general approach in three different concrete scenarios: a simulated robot arm that visually estimates the pose of an object of interest; a small differential drive robot using 7 infrared sensors to localize a nearby wall; an omnidirectional mobile robot that localizes itself in an environment from camera images. Quantitative results show that the approach works well in all three scenarios, and that explicitly accounting for uncertainty yields statistically significant performance improvements.

cs.RO↗

arXiv2021

VSync: Push-Button Verification and Optimization for Synchronization Primitives on Weak Memory Models (Technical Report)

This technical report contains material accompanying our work with same title published at ASPLOS'21. We start in Sec. 1 with a detailed presentation of the core innovation of this work, Await Model Checking (AMC). The correctness proofs of AMC can be found in Sec. 2. Next, we discuss three study cases in Sec. 3, presenting bugs found and challenges encountered when applying VSync to existing code bases. Finally, in Sec. 4 we describe the setup details of our evaluation and report further experimental results.

cs.LO↗

arXiv2020

Memory of Motion for Warm-starting Trajectory Optimization

Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide good initial guesses. The memory of motion relies on function approximators and dimensionality reduction techniques to learn the mapping between the tasks and the robot paths. Three function approximators are compared: $k$-Nearest Neighbor, Gaussian Process Regression, and Bayesian Gaussian Mixture Regression. In addition, we show that the memory can be used as a metric to choose between several possible goals, and using an ensemble method to combine different function approximators results in a significantly improved warm-starting performance. We demonstrate the proposed approach with motion planning examples on the dual-arm robot PR2 and the humanoid robot Atlas.

cs.RO↗

arXiv2020

A memory of motion for visual predictive control tasks

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.

cs.RO↗

arXiv2013

Energy Minimization for Parallel Real-Time Systems with Malleable Jobs and Homogeneous Frequencies

In this work, we investigate the potential utility of parallelization for meeting real-time constraints and minimizing energy. We consider malleable Gang scheduling of implicit-deadline sporadic tasks upon multiprocessors. We first show the non-necessity of dynamic voltage/frequency regarding optimality of our scheduling problem. We adapt the canonical schedule for DVFS multiprocessor platforms and propose a polynomial-time optimal processor/frequency-selection algorithm. We evaluate the performance of our algorithm via simulations using parameters obtained from a hardware testbed implementation. Our algorithm has up to a 60 watt decrease in power consumption over the optimal non-parallel approach.

cs.OS↗