24,950 research outputs found
MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning
This paper describes a framework called MaestROB. It is designed to make the
robots perform complex tasks with high precision by simple high-level
instructions given by natural language or demonstration. To realize this, it
handles a hierarchical structure by using the knowledge stored in the forms of
ontology and rules for bridging among different levels of instructions.
Accordingly, the framework has multiple layers of processing components;
perception and actuation control at the low level, symbolic planner and Watson
APIs for cognitive capabilities and semantic understanding, and orchestration
of these components by a new open source robot middleware called Project Intu
at its core. We show how this framework can be used in a complex scenario where
multiple actors (human, a communication robot, and an industrial robot)
collaborate to perform a common industrial task. Human teaches an assembly task
to Pepper (a humanoid robot from SoftBank Robotics) using natural language
conversation and demonstration. Our framework helps Pepper perceive the human
demonstration and generate a sequence of actions for UR5 (collaborative robot
arm from Universal Robots), which ultimately performs the assembly (e.g.
insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018.
Video: https://www.youtube.com/watch?v=19JsdZi0TW
Multi-level manual and autonomous control superposition for intelligent telerobot
Space telerobots are recognized to require cooperation with human operators in various ways. Multi-level manual and autonomous control superposition in telerobot task execution is described. The object model, the structured master-slave manipulation system, and the motion understanding system are proposed to realize the concept. The object model offers interfaces for task level and object level human intervention. The structured master-slave manipulation system offers interfaces for motion level human intervention. The motion understanding system maintains the consistency of the knowledge through all the levels which supports the robot autonomy while accepting the human intervention. The superposing execution of the teleoperational task at multi-levels realizes intuitive and robust task execution for wide variety of objects and in changeful environment. The performance of several examples of operating chemical apparatuses is shown
Model Learning for Look-ahead Exploration in Continuous Control
We propose an exploration method that incorporates look-ahead search over
basic learnt skills and their dynamics, and use it for reinforcement learning
(RL) of manipulation policies . Our skills are multi-goal policies learned in
isolation in simpler environments using existing multigoal RL formulations,
analogous to options or macroactions. Coarse skill dynamics, i.e., the state
transition caused by a (complete) skill execution, are learnt and are unrolled
forward during lookahead search. Policy search benefits from temporal
abstraction during exploration, though itself operates over low-level primitive
actions, and thus the resulting policies does not suffer from suboptimality and
inflexibility caused by coarse skill chaining. We show that the proposed
exploration strategy results in effective learning of complex manipulation
policies faster than current state-of-the-art RL methods, and converges to
better policies than methods that use options or parametrized skills as
building blocks of the policy itself, as opposed to guiding exploration. We
show that the proposed exploration strategy results in effective learning of
complex manipulation policies faster than current state-of-the-art RL methods,
and converges to better policies than methods that use options or parameterized
skills as building blocks of the policy itself, as opposed to guiding
exploration.Comment: This is a pre-print of our paper which is accepted in AAAI 201
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201
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