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Jinhan Lee

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.

3 papers

1 author row

IROS Conference 2009 Conference Paper

Effective Robot Task Learning by focusing on Task-relevant objects

Kyuhwa Lee
Jinhan Lee
Andrea Thomaz
Aaron F. Bobick

In a Robot Learning from Demonstration framework involving environments with many objects, one of the key problems is to decide which objects are relevant to a given task. In this paper, we analyze this problem and propose a biologically-inspired computational model that enables the robot to focus on the task-relevant objects. To filter out incompatible task models, we compute a Task Relevance Value (TRV) for each object, which shows a human demonstrator's implicit indication of the relevance to the task. By combining an intentional action representation with ‘motionese’ [2], our model exhibits recognition capabilities compatible with the way that humans demonstrate. We evaluate the system on demonstrations from five different human subjects, showing its ability to correctly focus on the appropriate objects in these demonstrations.

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ICRA Conference 2009 Conference Paper

Graph-based planning using local information for unknown outdoor environments

Jinhan Lee
Roozbeh Mottaghi
Charles Pippin
Tucker R. Balch

One of the common applications for outdoor robots is to follow a path in large scale unknown environments. This task is challenging due to the intensive memory requirements to represent the map, uncertainties in the location estimate of the robot and unknown terrain type and obstacles on the way to the goal. We develop a novel graph-based path planner that is based on only local perceptual information to plan a path in such environments. In order to extend the capabilities of the graph representation, we introduce Exploration Bias, which is a node attribute that can implicitly encode obstacle features at immediate surrounding of a node in the graph, the uncertainty of the planner about a node location and also the frequency of visiting a location. Through simulation experiments, we demonstrate that the resulting path cost and distance that the robot traverses to reach the goal location is not significantly different from those of the previous approaches.

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IROS Conference 2008 Conference Paper

Cost based planning with RRT in outdoor environments

Jinhan Lee
Charles Pippin
Tucker R. Balch

The Rapidly Exploring Random Tree (RRT) algorithm can be applied to the robotic path planning problem and performs well in challenging, dynamic domains. Traditional RRT methods use a binary cost function and they select portions of the tree for expansion based on the Euclidean distance to the target. However, in outdoor navigation, the relative cost of terrain can also provide useful input to a planning algorithm that traditional RRT methods cannot take advantage of. We present the Metric Adaptive RRT (MA-RRT), which integrates planning and fast execution for generating paths over a cost map. The MA-RRT algorithm considers underlying cost of a path when calculating the distance function for tree expansion. A heuristic value is also used for determining distance from a point to the target and an adaptive mechanism is employed for adjusting the heuristic on-line. We have implemented our approach in offline simulations and in outdoor robot experiments, and show that the MA-RRT algorithm can improve upon the quality of the path returned when cost is considered. The trade off between cost consideration and runtime performance is also presented.

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