33,519 research outputs found
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
Shaping in humans and animals has been shown to be a powerful tool for
learning complex tasks as compared to learning in a randomized fashion. This
makes the problem less complex and enables one to solve the easier sub task at
hand first. Generating a curriculum for such guided learning involves
subjecting the agent to easier goals first, and then gradually increasing their
difficulty. This paper takes a similar direction and proposes a dual curriculum
scheme for solving robotic manipulation tasks with sparse rewards, called
MaMiC. It includes a macro curriculum scheme which divides the task into
multiple sub-tasks followed by a micro curriculum scheme which enables the
agent to learn between such discovered sub-tasks. We show how combining macro
and micro curriculum strategies help in overcoming major exploratory
constraints considered in robot manipulation tasks without having to engineer
any complex rewards. We also illustrate the meaning of the individual curricula
and how they can be used independently based on the task. The performance of
such a dual curriculum scheme is analyzed on the Fetch environments.Comment: To appear in the Proceedings of the 18th International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2019). (Extended Abstract
Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks
A major challenge for the realization of intelligent robots is to supply them
with cognitive abilities in order to allow ordinary users to program them
easily and intuitively. One way of such programming is teaching work tasks by
interactive demonstration. To make this effective and convenient for the user,
the machine must be capable to establish a common focus of attention and be
able to use and integrate spoken instructions, visual perceptions, and
non-verbal clues like gestural commands. We report progress in building a
hybrid architecture that combines statistical methods, neural networks, and
finite state machines into an integrated system for instructing grasping tasks
by man-machine interaction. The system combines the GRAVIS-robot for visual
attention and gestural instruction with an intelligent interface for speech
recognition and linguistic interpretation, and an modality fusion module to
allow multi-modal task-oriented man-machine communication with respect to
dextrous robot manipulation of objects.Comment: 7 pages, 8 figure
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