Learning from Demonstration (LfD) is a popular approach to endowing robots with skills without having to program them by hand. Typically, LfD relies on human demonstrations in clutter-free environments. This prevents the demonstrations from being affected by irrelevant objects, whose influence can obfuscate the true intention of the human or the constraints of the desired skill. However, it is unrealistic to assume that the robot's environment can always be restructured to remove clutter when capturing human demonstrations. To contend with this problem, we develop an importance weighted batch and incremental skill learning approach, building on a recent inference-based technique for skill representation and reproduction. Our approach reduces unwanted environmental influences on the learned skill, while still capturing the salient human behavior. We provide both batch and incremental versions of our approach and validate our algorithms on a 7-DOF JACO2 manipulator with reaching and placing skills.
There have been a few attempts to address the problem of learning skills from demonstrations in cluttered environments. For example, @cite_2 @cite_0 learn a dynamic movement primitive (DMP) as well as a coupling term for obstacle avoidance from demonstrations. These approaches suffer from two major problems. First, since DMPs follow a single demonstration, they fail to learn potentially different ways of executing the skill, thereby limiting its robustness in new scenarios. Second, due to the reactive nature of the obstacle avoidance strategy, the reproduced trajectory does not necessarily preserve the shape of the motion in the presence of obstacles. @cite_5 , proposed an approach based on learning a cost functional from human demonstrations. This cost functional is dependent on two components: the deviation from the mean of the demonstrations, and the distance from obstacles in the environment. Parameters of both these components are estimated from human demonstrations. A major drawback of this approach is the assumption that the mean of the demonstrations sufficiently expresses the demonstrated skill. This assumption however stands invalid for skills which can be executed in multiple ways and hence requires a more expressive skill model.
@cite_0 @cite_5 @cite_2
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Dynamic Movement Primitives (DMPs) are a common method for learning a control policy for a task from demonstration. This control policy consists of differential equations that can create a smooth trajectory to a new goal point. However, DMPs only have a limited ability to generalize the demonstration to new environments and solve problems such as obstacle avoidance. Moreover, standard DMP learning does not cope with the noise inherent to human demonstrations. Here, we propose an approach for robot learning from demonstration that can generalize noisy task demonstrations to a new goal point and to an environment with obstacles. This strategy for robot learning from demonstration results in a control policy that incorporates different types of learning from demonstration, which correspond to different types of observational learning as outlined in developmental psychology. Autonomous manipulation in dynamic environments is important for robots to perform everyday tasks. For this, a manipulator should be capable of interpreting the environment and planning an appropriate movement. At least, two possible approaches exist for this in literature. Usually, a planning system is used to generate a complex movement plan that satisfies all constraints. Alternatively, a simple plan could be chosen and modified with sensory feedback to accommodate additional constraints by equipping the controller with features that remain dormant most of the time, except when specific situations arise. Dynamic Movement Primitives (DMPs) form a robust and versatile starting point for such a controller that can be modified online using a non-linear term, called the coupling term. This can prove to be a fast and reactive way of obstacle avoidance in a human-like fashion. We propose a method to learn this coupling term from human demonstrations starting with simple features and making it more robust to avoid a larger range of obstacles. We test the ability of our coupling term to model different kinds of obstacle avoidance behaviours in humans and use this learnt coupling term to avoid obstacles in a reactive manner. This line of research aims at pushing the boundary of reactive control strategies to more complex scenarios, such that complex and usually computationally more expensive planning methods can be avoided as much as possible.