3,708 research outputs found
Generalized Task-Parameterized Skill Learning
Programming by demonstration has recently gained much attention due to its user-friendly and natural way to transfer human skills to robots. In order to facilitate the learning of multiple demonstrations and meanwhile generalize to new situations, a task-parameterized Gaussian mixture model (TP-GMM) has been recently developed. This model has achieved reliable performance in areas such as human-robot collaboration and dual-arm manipulation. However, the crucial task frames and associated parameters in this learning framework are often set by the human teacher, which renders three problems that have not been addressed yet: (i) task frames are treated equally, without considering their individual importance, (ii) task parameters are defined without taking into account additional task constraints, such as robot joint limits and motion smoothness, and (iii) a fixed number of task frames are pre-defined regardless of whether some of them may be redundant or even irrelevant for the task at hand. In this paper, we generalize the task-parameterized learning by addressing the aforementioned problems. Moreover, we provide a novel learning perspective which allows the robot to refine and adapt previously learned skills in a low dimensional space. Several examples are studied in both simulated and real robotic systems, showing the applicability of our approach
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
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
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