RSS 2019 Papers

Robotics: Science and Systems

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84papers

Counted from the official paper list; may differ slightly from the number the conference announced.

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A Dynamical System Approach to Motion and Force Generation in Contact Tasks

Walid Amanhoud, Mahdi Khoramshahi, Aude Billard

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Many tasks require the robot to enter in contact with surfaces, be it to take support, to polish or to grasp an object. It is crucial that the robot controls forces both upon making contact and while in contact. While many solutions exist to control for contact, none offer the required robustness to adapt to real-world uncertainties, such as sudden displacement of the object prior and once in contact. To adapt to such disturbances require to re-plan on the fly both the trajectory and the force. Dynamical systems (DS) offer a framework for instant re-planning of trajectories. They are however limited to control of motions. We extend this framework here to enable generating contact forces and trajectories through DS. The framework allows also to modulate the impedance so as to show rigidity to maintain contact, and compliance to ensure safe interaction with humans. We validate the approach in single and dual arm setting using KUKA LWR 4+ robotic arms. We show that the approach allows 1) to make smooth contact while applying large forces, 2) to maintain desired contact force when scanning non-linear surfaces, even when the surface is moved, and 3) to grasp and lift smoothly an object in the air, and to re-balance forces on the fly to maintain the grasp even when subjected to strong external disturbances.

Poster
A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization

Benoit Landry, Zachary Manchester, Marco Pavone

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Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization solver that is well suited to bilevel optimization. We then demonstrate the validity and scalability of our algorithm with two representative robotic problems, namely robust control and parameter estimation for a system involving contact. We stress the general nature of the algorithm and its potential relevance to many other problems in robotics.

Poster
A Behavioral Approach to Visual Navigation with Graph Localization Networks

Kevin Chen, Juan Pablo de Vicente, Gabriel Sepulveda, Fei Xia, Alvaro Soto, Marynel Vázquez, Silvio Savarese

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Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps. Our goal is to enable a robot to navigate from one location to another, relying only on its visual observations and the topological map of the environment. To this end, we propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks. Using the Gibson simulator and the Stanford 2D-3D-S dataset, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen indoor environments.

Poster
A 2-Approximation Algorithm for the Online Tethered Coverage Problem

Gokarna Sharma, Pavan Poudel, Ayan Dutta, Vala Zeinali, Tala.Talaei Khoei, Jong-Hoon Kim

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We consider the problem of covering a planar environment, possibly containing unknown obstacles, using a robot of square size D x D attached to a fixed point S by a cable of finite length L. The environment is discretized into 4-connected grid cells with resolution proportional to the robot size. Starting at S, the task of the robot is to visit each cell in the environment that are not occupied by obstacles and return to S with the cable fully retracted. Our goal is to minimize the total distance traveled by the robot to fully cover the unknown environment while avoiding tangling of the cable. In this paper, we present a novel online algorithm to solve this problem that achieves 2-approximation for the total distance traveled by the robot compared to the minimum distance that needs to be traveled. Our algorithm significantly improves the 2L/D-approximation achieved by the best previously known online algorithm designed for this problem. The approximation bound is also validated using rigorous simulated experiments.

Poster