Author name cluster

Nik A. Melchior

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

IROS Conference 2012 Conference Paper

Graph-based trajectory planning through programming by demonstration

Nik A. Melchior
Reid G. Simmons

As robots are utilized in a growing number of applications, the ability to teach them to perform tasks safely and accurately becomes ever more critical. Programming by demonstration offers an expressive means for teaching while being accessible to domain experts who may be novices in robotics. This work investigates a programming by demon- stration approach to learning motion trajectories for robotic manipulator tasks. Using a graph constructed to determine correspondences between multiple imperfect demonstrations, the robot learner plans novel trajectories that safely and smoothly generalize the teacher's behavior, while attenuating those imperfections. The learner also actively detects instances of diverging strategy between examples, requesting advice for resolving these ambiguities. We demonstrate our approach in example domains with a 7 degree-of-freedom manipulator.

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ICRA Conference 2010 Conference Paper

Dimensionality reduction for trajectory learning from demonstration

Nik A. Melchior
Reid G. Simmons

Programming by demonstration is an attractive model for allowing both experts and non-experts to command robots' actions. In this work, we contribute an approach for learning precise reaching trajectories for robotic manipulators. We use dimensionality reduction to smooth the example trajectories and transform their representation to a space more amenable to planning. Key to this approach is the careful selection of neighboring points within and between trajectories. This algorithm is capable of creating efficient, collision-free plans even under typical real-world training conditions such as incomplete sensor coverage and lack of an environment model, without imposing additional requirements upon the user such as constraining the types of example trajectories provided. Experimental results are presented to validate this approach.

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IROS Conference 2008 Conference Paper

Overcoming sensor noise for low-tolerance autonomous assembly

Brennan Sellner
Frederik W. Heger
Laura M. Hiatt
Nik A. Melchior
Stephen N. Roderick
Dave Akin
Reid G. Simmons
Sanjiv Singh

The capability to assemble structures is fundamental to the use of robotics in precursor missions in orbit and on planetary surfaces. We have performed autonomous assembly in neutral buoyancy of elements of a space truss whose mating components require positioning tolerances of the same order of magnitude as the noise in the sensor systems used for the docking. Numerous trade-offs, design decisions, and innovations were made during the development of the assembly system in order to both reduce and compensate for the sensor noise. By using relative positioning, decoupling sensing and manipulation, caching high-quality position estimates, and developing a new waypoint-completion metric, we were able to reduce sensor noise to the sub-millimeter level and autonomously assemble components with millimeter tolerances. In this paper, we discuss our approaches to the problem and report the results of a series of autonomous assembly operations.

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ICRA Conference 2007 Conference Paper

Particle RRT for Path Planning with Uncertainty

Nik A. Melchior
Reid G. Simmons

This paper describes a new extension to the rapidly-exploring random tree (RRT) path planning algorithm. The particle RRT algorithm explicitly considers uncertainty in its domain, similar to the operation of a particle filter. Each extension to the search tree is treated as a stochastic process and is simulated multiple times. The behavior of the robot can be characterized based on the specified uncertainty in the environment, and guarantees can be made as to the performance under this uncertainty. Extensions to the search tree, and therefore entire paths, may be chosen based on the expected probability of successful execution. The benefit of this algorithm is demonstrated in the simulation of a rover operating in rough terrain with unknown coefficients of friction

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