Jonathan Ko

ICRA Conference 2009 Conference Paper

Anatomically correct testbed hand control: Muscle and joint control strategies

• Ashish D. Deshpande
• Jonathan Ko
• Dieter Fox
• Yoky Matsuoka

Human hands are capable of many dexterous grasping and manipulation tasks. To understand human levels of dexterity and to achieve it with robotic hands, we constructed an anatomically correct testbed (ACT) hand which allows for the investigation of the biomechanical features and neural control strategies of the human hand. This paper focuses on developing control strategies for the index finger motion of the ACT Hand. A direct muscle position control and a force-optimized joint control are implemented as building blocks and tools for comparisons with future biological control approaches. We show how Gaussian process regression techniques can be used to determine the relationships between the muscle and joint motions in both controllers. Our experiments demonstrate that the direct muscle position controller allows for accurate and fast position tracking, while the force-optimized joint controller allows for exploitation of actuation redundancy in the finger critical for this redundant system. Furthermore, a comparison between Gaussian processes and least squares regression method shows that Gaussian processes provide better parameter estimation and tracking performance. This first control investigation on the ACT hand opens doors to implement biological strategies observed in humans and achieve the ultimate human-level dexterity.

IROS Conference 2008 Conference Paper

GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models

• Jonathan Ko
• Dieter Fox

Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filters (extended and unscented) and particle filters. Key components of each Bayes filter are probabilistic prediction and observation models. Recently, Gaussian processes have been introduced as a non-parametric technique for learning such models from training data. In the context of unscented Kalman filters, these models have been shown to provide estimates that can be superior to those achieved with standard, parametric models. In this paper we show how Gaussian process models can be integrated into other Bayes filters, namely particle filters and extended Kalman filters. We provide a complexity analysis of these filters and evaluate the alternative techniques using data collected with an autonomous micro-blimp.

ICRA Conference 2007 Conference Paper

Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous Blimp

• Jonathan Ko
• Daniel J. Klein
• Dieter Fox
• Dirk Hähnel

Blimps are a promising platform for aerial robotics and have been studied extensively for this purpose. Unlike other aerial vehicles, blimps are relatively safe and also possess the ability to loiter for long periods. These advantages, however, have been difficult to exploit because blimp dynamics are complex and inherently non-linear. The classical approach to system modeling represents the system as an ordinary differential equation (ODE) based on Newtonian principles. A more recent modeling approach is based on representing state transitions as a Gaussian process (GP). In this paper, we present a general technique for system identification that combines these two modeling approaches into a single formulation. This is done by training a Gaussian process on the residual between the non-linear model and ground truth training data. The result is a GP-enhanced model that provides an estimate of uncertainty in addition to giving better state predictions than either ODE or GP alone. We show how the GP-enhanced model can be used in conjunction with reinforcement learning to generate a blimp controller that is superior to those learned with ODE or GP models alone.

IROS Conference 2007 Conference Paper

GP-UKF: Unscented kalman filters with Gaussian process prediction and observationmodels

• Jonathan Ko
• Daniel J. Klein
• Dieter Fox
• Dirk Hähnel

This paper considers the use of non-parametric system models for sequential state estimation. In particular, motion and observation models are learned from training examples using Gaussian process (GP) regression. The state estimator is an unscented Kalman filter (UKF). The resulting GP-UKF algorithm has a number of advantages over standard (parametric) UKFs. These include the ability to estimate the state of arbitrary nonlinear systems, improved tracking quality compared to a parametric UKF, and graceful degradation with increased model uncertainty. These advantages stem from the fact that GPs consider both the noise in the system and the uncertainty in the model. If an approximate parametric model is available, it can be incorporated into the GP; resulting in further performance improvements. In experiments, we show how the GP-UKF algorithm can be applied to the problem of tracking an autonomous micro-blimp.

AAAI Conference 2004 System Paper

Centibots: Very Large Scale Distributed Robotic Teams

• Charlie Ortiz
• Regis Vincent
• Andrew Agno
• Dieter Fox
• Jonathan Ko

In this paper, we describe the development of Centibots, a framework for very large teams of robots that are able to perceive, explore, plan and collaborate in unknown environments. Teams consist of approximately 100 robots which can be deployed in unexplored areas and which can efficiently distribute tasks among themselves; the system also makes use of a mixed initiative mode of interaction in which a user can easily influence missions as necessary. In contrast to simulation-based systems which abstract away aspects of the environment for examining component technologies, our design reflects an integrated, end-to-end system. Fex

IROS Conference 2003 Conference Paper

A practical, decision-theoretic approach to multi-robot mapping and exploration

• Jonathan Ko
• Benjamin Stewart
• Dieter Fox
• Kurt Konolige
• Benson Limketkai

An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the relative locations of the individual robots. This paper presents a novel approach to multi-robot map merging under global uncertainty about the robot's relative locations. Our approach uses an adapted version of particle filters to estimate the position of one robot in the other robot's partial map. The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach. We show how to seamlessly integrate this approach into a decision-theoretic multi-robot coordination strategy. The experiments show that our sample-based technique can reliably find good hypotheses for map matches. Furthermore, we present results obtained with two robots successfully merging their maps using the decision-theoretic rendezvous strategy.

IROS Conference 2003 Conference Paper

Map merging for distributed robot navigation

• Kurt Konolige
• Dieter Fox
• Benson Limketkai
• Jonathan Ko
• Benjamin Stewart

A set of robots mapping an area can potentially combine their information to produce a distributed map more efficiently than a single robot alone. We describe a general framework for distributed map building in the presence of uncertain communication. Within this framework, we then present a technical solution to the key decision problem of determining relative location within partial maps.

UAI Conference 2003 Conference Paper

The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach

• Benjamin Stewart
• Jonathan Ko
• Dieter Fox
• Kurt Konolige