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Most of the available robot programming by demonstration (PbD) approaches focus on learning a single task, in a given environmental situation. In this paper, we propose to learn multiple tasks together, within a common environment, using one of the available PbD approaches. Task-parameterized Gaussian mixture model (TP-GMM) is used at the core of the proposed approach. A database of TP-GMMs will be...
This work addresses a technique for robot motion planning and navigation in dynamic environments. First, a model to represent the future evolution of the moving obstacles in the environment is defined in a robocentric reference, which maps the obstacle motion on the control space, the velocity-time space, of the robot. Second, a planning technique working on that model for navigation and maneuvering...
This paper explores the problem of path planning under uncertainty. Specifically, we consider online receding horizon based planners that need to operate in a latent environment where the latent information can be modelled via Gaussian Processes. Online path planning in latent environments is challenging since the robot needs to explore the environment to get a more accurate model of latent information...
We present an approach to learning control policies for physical robots that achieves high efficiency by adjusting existing policies that have been learned on similar source systems, such as a similar robot with different physical parameters, or an approximate dynamics model simulator. This can be viewed as calibrating a policy learned on a source system, to match a desired behaviour in similar target...
Robotic agents that do everyday manipulation tasks can hugely benefit from being able to predict consequences of their actions just before the execution. However, such a simulation technique is usually computationally-expensive and may not be achieved with agents' self computing power. For this problem, cloud robotics may offer a solution. Cloud robotics is an emerging field in the intersection of...
Automatic identification of the relevant frames of references (or external task parameters) in programming by demonstration using the task-parameterized Gaussian mixture regression (TP-GMM) is addressed in this paper. While performing a given task, there may be several external task parameters, some of which are relevant to the specific task, while some others are not relevant. Identifying the irrelevant...
The increased diffusion of service robots operating in tight collaboration with humans has renewed the interest of the scientific community towards realistic human motion models. In this paper, we present the Headed Social Force Model, a modeling approach enriching Helbing's Social ForceModel with Laumond's human locomotion models. The proposed solution is shown to inherit the best features of either...
In this work, a simple model is used to characterize the learning behaviour of humans. Based on this model, it is possible to define a similarity measure between two tasks in order to quantify skill generalisation during the learning of simple motor tasks by humans. By fully exploring this similarity measure, a sequence of tasks capable of improving the learning efficiency for both healthy subjects...
Policy Gradient methods require many real-world trials. Some of the trials may endanger the robot system and cause its rapid wear. Therefore, a safe or at least gentle-to-wear exploration is a desired property. We incorporate bounds on the probability of unwanted trials into the recent Contextual Relative Entropy Policy Search method. The proposed algorithm is evaluated on the task of autonomous flipper...
Model-based trajectory optimization often fails to find a reference trajectory for under-actuated bipedal robots performing highly-dynamic, contact-rich tasks in the real world due to inaccurate physical models. In this paper, we propose a complete system that automatically designs a reference trajectory that succeeds on tasks in the real world with a very small number of real world experiments. We...
This paper reports on a data-driven motion planning approach for interaction-aware, socially-compliant robot navigation among human agents. Autonomous mobile robots navigating in workspaces shared with human agents require motion planning techniques providing seamless integration and smooth navigation in such. Smooth integration in mixed scenarios calls for two abilities of the robot: predicting actions...
We present an optimization-based motion planning algorithm to compute a smooth, collision-free trajectory for a manipulator used to transfer a liquid from a source to a target container. We take into account fluid dynamics constraints as part of the trajectory computation. In order to avoid the high complexity of exact fluid simulation, we introduce a simplified dynamics model based on physically...
In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. One often used strategy considers the user and autonomy as independent decision makers, with the system blending these decisions. However, this independence leads to suboptimal, and often frustrating, behavior. Instead, we propose a system that explicitly models the interplay between the user and assistance...
Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In...
This paper describes a novel offline approach to estimate the parameters of a nonlinear system using the optimal control theory. In the conventional methods, only the output is considered in the identification process and the problem is stated as a nonlinear optimization problem. Nevertheless, the proposed method here is based on the least square error of both output and input. To do this, the problem...
This paper presents a novel manipulation trajectory generating algorithm that constructs trajectories from learned motion harmonics and user defined constraints. The algorithm uses functional eigenanalysis to learn motion harmonics from demonstrated motions and then use the motion harmonics to compute the optimal trajectory that resembles the demonstrated motions and also satisfies the constraints...
The robotic servicing of satellites on-orbit will play an important role in extending their lifespan and mitigating space debris expansion. However, most maneuvers related to these goals present a set of unique challenges which are hard to assess in reality. On the other hand, a reliable prediction of the feasibility of servicing maneuvers is increasingly required. This paper presents a simulation-driven...
This paper presents the details and experimental results from an implementation of real-time trajectory generation and parameter estimation of a dynamic model using the Baxter Research Robot from Rethink Robotics. Trajectory generation is based on the maximization of Fisher information in real-time and closed-loop using a form of Sequential Action Control. On-line estimation is performed with a least-squares...
Therapy environments for robot-assisted stroke rehabilitation are mostly static, in that objects are earmarked for different functional tasks, object locations remain fixed, trajectories are predefined and tasks manually selected. This work advocates the need for a therapy environment that allows dynamic object positioning, different objects can be used for the same functional task and tasks can be...
Exploration and self-directed learning are valuable components of early childhood development. This often comes at an unacceptable safety trade-off, as infants and toddlers are especially at risk from environmental hazards that may fundamentally limit their ability to interact with and explore their environments. In this work we address this risk through the incorporation of a caregiver robot, and...
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