Author name cluster

Jonathan Binney

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

ICRA Conference 2013 Conference Paper

Learning task error models for manipulation

Peter Pastor
Mrinal Kalakrishnan
Jonathan Binney
Jonathan Kelly
Ludovic Righetti
Gaurav S. Sukhatme
Stefan Schaal

Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipulation tasks, especially when visual servoing is rendered infeasible due to occlusions. A lot of research has been conducted to estimate geometric and non-geometric parameters of kinematic chains to minimize reconstruction errors. However, kinematic chains can include non-linearities, e. g. due to cable stretch and motor-side encoders, that result in significantly different errors for different parts of the state space. Previous work either does not consider such non-linearities or proposes to estimate non-geometric parameters of carefully engineered models that are robot specific. We propose a data-driven approach that learns task error models that account for such unmodeled non-linearities. We argue that in the context of grasping and manipulation, it is sufficient to achieve high accuracy in the task relevant state space. We identify this relevant state space using previously executed joint configurations and learn error corrections for those. Therefore, our system is developed to generate subsequent executions that are similar to previous ones. The experiments show that our method successfully captures the non-linearities in the head kinematic chain (due to a counterbalancing spring) and the arm kinematic chains (due to cable stretch) of the considered experimental platform, see Fig. 1. The feasibility of the presented error learning approach has also been evaluated in independent DARPA ARM-S testing contributing to successfully complete 67 out of 72 grasping and manipulation tasks.

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ICRA Conference 2012 Conference Paper

Branch and bound for informative path planning

Jonathan Binney
Gaurav S. Sukhatme

We present an optimal algorithm for informative path planning (IPP), using a branch and bound method inspired by feature selection algorithms. The algorithm uses the monotonicity of the objective function to give an objective function-dependent speedup versus brute force search. We present results which suggest that when maximizing variance reduction in a Gaussian process model, the speedup is significant.

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IROS Conference 2011 Conference Paper

Toward risk aware mission planning for Autonomous Underwater Vehicles

Arvind Pereira
Jonathan Binney
Burton H. Jones
Matthew Ragan
Gaurav S. Sukhatme

Long range and high endurance Autonomous Underwater Vehicles such as gliders enable sustained oceanographic sampling at larger time-scales and much lower operational costs compared to traditional ship-based sampling methods. While most path-planning methods for AUVs optimize paths with respect to efficiency, obstacle avoidance, and control they do not explicitly address the issue of finding the safest possible path when considering risks such as shipping traffic and bathymetry. In coastal regions with high shipping traffic, reducing collision risk at the path planning stage, at the expense of efficiency, is a worthwhile trade-off. We propose a method of building risk maps using historical data from the Automated Information System. These are used to plan minimum risk paths between a specified start and goal location, while avoiding obstacles, using an algorithm based on A* search. Our planner incorporates the uncertainty in dead-reckoning without explicitly considering the effect of ocean currents. We compare the relative risk of paths produced by our method when compared to a shortest-path planner which does not take risk into account, and show that our methods performs significantly better, while producing competitive paths lengths.

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ICRA Conference 2010 Conference Paper

Informative path planning for an autonomous underwater vehicle

Jonathan Binney
Andreas Krause 0001
Gaurav S. Sukhatme

We present a path planning method for autonomous underwater vehicles in order to maximize mutual information. We adapt a method previously used for surface vehicles, and extend it to deal with the unique characteristics of underwater vehicles. We show how to generate near-optimal paths while ensuring that the vehicle stays out of high-traffic areas during predesignated time intervals. In our objective function we explicitly account for the fact that underwater vehicles typically take measurements while moving, and that they do not have the ability to communicate until they resurface. We present field results from ocean trials on planning paths for a specific AUV, an underwater glider.

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ICRA Conference 2009 Conference Paper

3D tree reconstruction from laser range data

Jonathan Binney
Gaurav S. Sukhatme

We present a method for reconstructing 3D models of tree branch structure from laser range data. Our approach is probabilistic, and uses general knowledge of tree structure to guide an iterative reconstruction process. Our goal is to recover parameters such as branch locations, angles, radii, and lengths, as well as connectivity information between branches. These parameters can then be fed into functional-structural plant models to study the relationships between the structure of a plant, its environment, and its internal biology. In this paper we present an algorithm for finding these parameters, and results on both simulated and real datasets.

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