Learning Plans with Context
From Human Signals



Berkeley, July 13th (Room# Wheeler-123)

In conjunction with RSS 2014


Abstract Call for papers Important Dates Schedule Contact

This is a full-day workshop to be held in conjunction with the Robotics Science and Systems (RSS) conference 2014, in Berkeley, July 13th. This workshop aims at a broader audience and will bring together people from machine learning, planning and HRI communities.

Human environments such as homes, warehouses and offices are very rich with the context of the objects and humans present and the task to be performed. Robots should incorporate this rich context and plan human-preferred motions. Furthermore, the robots should learn from different kinds of human feedback, which can range from optimal demonstrations to sub-optimal incremental signals. From an HRI perspective, signals come in various forms and we need new machine learning techniques to use these signals for meaningful motion planning. Through this workshop we bring together people from the areas of machine learning, planning and HRI to discuss how robots can learn to plan and act in context-rich human environments.

Invited Speakers


Pieter Abbeel

UC Berkeley

Alan Fern

OSU

Manuel Lopes

INRIA

Maja Mataric

USC

Julie Shah

MIT

Andrea Thomaz

GaTech

Organizers


Drew Bagnell

CMU

Ashesh Jain

Cornell

Jan Peters

TU Darmstadt

Ashutosh Saxena

Cornell