Learning skills from kinesthetic demonstrations is a promising way of minimizing the gap between human manipulation abilities and those of robots. We propose an approach to learn sequential force interaction skills from such demonstrations. The demonstrations are decomposed into a set of movement primitives by inferring the underlying sequential structure of the task. The decomposition is based on a novel probability distribution which we call Directional Normal Distribution. The distribution allows infering the movement primitive's composition, i.e., its coordinate frames, control variables and target coordinates from the demonstrations. In addition, it permits determining an appropriate number of movement primitives for a task via model selection. After finding the task's composition, the system learns to sequence the resulting movement primitives in order to be able to reproduce the task on a real robot. We evaluate the approach on three different tasks, unscrewing a light bulb, box stacking and box flipping. All tasks are kinesthetically demonstrated and then reproduced on a Barrett WAM robot.

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Robot learning problems are limited by physical constraints, which make learning successful policies for complex motor skills on real systems unfeasible. Some reinforcement learning methods, like Policy Search, offer stable convergence toward locally optimal solutions, whereas interactive machine learning or learning-from-demonstration methods allow fast transfer of human knowledge to the agents. However, most methods require expert demonstrations. In this work, we propose the use of human corrective advice in the actions domain for learning motor trajectories. Additionally, we combine this human feedback with reward functions in a Policy Search learning scheme. The use of both sources of information speeds up the learning process, since the intuitive knowledge of the human teacher can be easily transferred to the agent, while the Policy Search method with the cost/reward function take over for supervising the process and reducing the influence of occasional wrong human corrections. This interactive approach has been validated for learning movement primitives with simulated arms with several degrees of freedom in reaching via-point movements, and also using real robots in such tasks as “writing characters” and the ball-in-a-cup game. Compared with standard reinforcement learning without human advice, the results show that the proposed method not only converges to higher rewards when learning movement primitives, but also that the learning is sped up by a factor of 4–40 times, depending on the task.@en

In this letter, we introduce Mixture of Attractors, a novel movement primitive representation that allows for learning complex object-relative movements. The movement primitive representation inherently supports multiple coordinate frames, enabling the system to generalize a skill to unseen object positions and orientations. In contrast to most other approaches, a skill is learned by solving a convex optimization problem. Therefore, the quality of the skill does not depend on a good initial estimate of parameters. The resulting movements are automatically smooth and can be of arbitrary shape. The approach is evaluated and compared to other movement primitive representations on data from the Omniglot handwriting dataset and on real demonstrations of a handwriting task. The evaluations show that the presented approach outperforms other state-of-the-art concepts in terms of generalization capabilities and accuracy.

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Learning sequential force interaction tasks from kinesthetic demonstrations is a promising approach to transfer human manipulation abilities to a robot. In this paper we propose a novel concept to decompose such demonstrations into a set of Movement Primitives (MPs). The decomposition is based on a probability distribution we call Directional Normal Distribution (DND). To capture the sequential properties of the manipulation task, we model the demonstrations with a Hidden Markov Model (HMM). Here, we employ mixtures of DNDs as the HMM's output emissions. The combination of HMMs and mixtures of DNDs allows to infer the MP's composition, i.e., its coordinate frames, control variables and target coordinates from the demonstration data. In addition, it permits to determine an appropriate number of MPs that explains the demonstrations best. We evaluate the approach on kinesthetic demonstrations of a light bulb unscrewing task. Decomposing the task leads to intuitive and meaningful MPs that reflect the natural structure of the task.

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While ships evolve constantly, berthing velocity curves developed during the 1970s are still embedded in the design of marine structures. This paper discusses the interpretation of new berthing records of modern large seagoing vessels collected in the port of Rotterdam. Berthing velocities of several types of vessels at various berths and operational conditions were examined, resulting in an increased understanding of the relevant aspects and the establishment of new probability distribution functions. Navigation conditions were accounted for by differentiating factors such as vessel characteristics (size and actual draft), environmental conditions (currents, wind and waves) and the berthing policy (pilot and tug assistance). The measured berthing velocities are most sensitive to the general berthing policy and local experience of pilots. Due to newly acquired insights, some historically embedded hypotheses will need to be reconsidered. For instance, the assumption that berthing velocities are strongly correlated to the size of the vessels could not be confirmed for ships larger than 50,000 DWT. Also, no water cushion effect was observed. The manoeuvring of container vessels showed a high sensitivity to wind, but their berthing velocities were not correlated to lateral wind power at all. Extremely low berthing angles of large seagoing container vessels were observed at the moment of berthing impact. The results of this research are of added value to all ports with similar sheltered navigation conditions and berthing policy to Rotterdam. The key findings were discussed with marine engineers, asset managers, harbour masters and pilots. This type of validation provided a better understanding of berthing velocity to all experts. The lower design berthing velocities will be beneficial for future structural assessments and lifetime extension of marine structures. @en