Author name cluster

Colin Lea

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.

4 papers

1 author row

ICRA Conference 2016 Conference Paper

Learning convolutional action primitives for fine-grained action recognition

Colin Lea
René Vidal
Gregory D. Hager

Fine-grained action recognition is important for many applications of human-robot interaction, automated skill assessment, and surveillance. The goal is to segment and classify all actions occurring in a time series sequence. While recent recognition methods have shown strong performance in robotics applications, they often require hand-crafted features, use large amounts of domain knowledge, or employ overly simplistic representations of how objects change throughout an action. In this paper we present the Latent Convolutional Skip Chain Conditional Random Field (LC-SC-CRF). This time series model learns a set of interpretable and composable action primitives from sensor data. We apply our model to cooking tasks using accelerometer data from the University of Dundee 50 Salads dataset and to robotic surgery training tasks using robot kinematic data from the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our performance on 50 Salads and JIGSAWS are 18. 0% and 5. 3% higher than the state of the art, respectively. This model performs well without requiring hand-crafted features or intricate domain knowledge. The code and features have been made public.

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IROS Conference 2016 Conference Paper

Sensor substitution for video-based action recognition

Christian Rupprecht 0001
Colin Lea
Federico Tombari
Nassir Navab
Gregory D. Hager

There are many applications where domain-specific sensing, such as accelerometers, kinematics, or force sensing, provide unique and important information for control or for analysis of motion. However, it is not always the case that these sensors can be deployed or accessed beyond laboratory environments. For example, it is possible to instrument humans or robots to measure motion in the laboratory in ways that it is not possible to replicate in the wild. An alternative, which we explore in this paper, is to address situations where accurate sensing is available while training an algorithm, but for which only video is available for deployment. We present two examples of this sensory substitution methodology. The first variation trains a convolutional neural network to regress real-valued signals, including robot end-effector pose, from video. The second example regresses binary signals derived from accelerometer data which signifies when specific objects are in motion. We evaluate these on the JIGSAWS dataset for robotic surgery training assessment and the 50 Salads dataset for modeling complex structured cooking tasks. We evaluate the trained models for video-based action recognition and show that the trained models provide information that is comparable to the sensory signals they replace.

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ICRA Conference 2015 Conference Paper

A framework for end-user instruction of a robot assistant for manufacturing

Kelleher Guerin
Colin Lea
Chris Paxton 0001
Gregory D. Hager

Small Manufacturing Entities (SMEs) have not incorporated robotic automation as readily as large companies due to rapidly changing product lines, complex and dexterous tasks, and the high cost of start-up. While recent low-cost robots such as the Universal Robots UR5 and Rethink Robotics Baxter are more economical and feature improved programming interfaces, based on our discussions with manufacturers further incorporation of robots into the manufacturing work flow is limited by the ability of these systems to generalize across tasks and handle environmental variation. Our goal is to create a system designed for small manufacturers that contains a set of capabilities useful for a wide range of tasks, is both powerful and easy to use, allows for perceptually grounded actions, and is able to accumulate, abstract, and reuse plans that have been taught. We present an extension to Behavior Trees that allows for representing the system capabilities of a robot as a set of generalizable operations that are exposed to an end-user for creating task plans. We implement this framework in CoSTAR, the Collaborative System for Task Automation and Recognition, and demonstrate its effectiveness with two case studies. We first perform a complex tool-based object manipulation task in a laboratory setting. We then show the deployment of our system in an SME where we automate a machine tending task that was not possible with current off the shelf robots.

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IROS Conference 2011 Conference Paper

Comparative evaluation of range sensing technologies for underground void modeling

Uland Y. Wong
Aaron Morris
Colin Lea
James Lee
Chuck Whittaker
Ben Garney
William Whittaker

This paper compares a broad cross-section of range sensing technologies for underground void modeling. In this family of applications, a tunnel environment is incrementally mapped with range sensors from a mobile robot to recover scene geometry. Distinguishing contributions of this work include an unprecedented number of configurations evaluated utilizing common methodology and metrics as well as a significant in situ environmental component lacking in prior characterization work. Sensors are experimentally compared against both an ideal geometric target and in example void environments such as a mine and underground tunnel. Three natural groupings of sensors were identified from these results and performances were found to be strongly cost-correlated. While the results presented are specific to the experimental configurations tested, the generality of tunnel environments and the metrics of reconstruction are extensible to a spectrum of outdoor and surface applications.

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