Abstract-Aiming at the application in physical human-robot interaction, this paper presents a novel adaptive admittance control scheme for robotic manipulators. Special emphasis is drawn on the avoidance of oscillatory behavior in the presence of closed kinematic chains while keeping the rendered impedance low. The approach uses an online fast Fourier transform of the measured manipulator endeffector forces in order to detect oscillations and to adapt the admittance parameters dynamically. As a novel method towards humancentered control design the adaptation strategy is determined in a user study evaluated with a machine-learning algorithm. Experiments conducted with ten human participants show superiority over the non-adaptive admittance control scheme.

Assuming that a robot trajectory is given from a high-level planning or learning mechanism, it needs to be adapted to react to dynamic environment changes. In this article we propose a novel approach to deform trajectories while keeping their local shape similar, which is based on the discrete Laplace-Beltrami operator. The approach can be readily extended and covers multiple deformation techniques including fixed waypoints that must be passed, positional constraints for collision avoidance or a cooperative manipulation scheme for the coordination of multiple robots. Due to its low computational complexity it allows for realtime trajectory deformation both on local and global scale and online adaptation to changed environmental constraints. Simulations illustrate the straightforward combination of the proposed approach with other established trajectory-related methods like artificial potential fields or prioritized inverse kinematics. Experiments with the HRP-4 humanoid successfully demonstrate the applicability in complex daily-life tasks. Electronic supplementary materialThe online version of this article (

Ongoing technological advances in the areas of computation, sensing, and mechatronics enable robotic-based systems to interact with humans in the real world. To succeed against a human in a competitive scenario, a robot must anticipate the human behavior and include it in its own planning framework. Then it can predict the next human move and counter it accordingly, thus not only achieving overall better performance but also systematically exploiting the opponent's weak spots. Pool is used as a representative scenario to derive a model-based planning and control framework where not only the physics of the environment but also a model of the opponent is considered. By representing the game of pool as a Markov decision process and incorporating a model of the human decision-making based on studies, an optimized policy is derived. This enables the robot to include the opponent's typical game style into its tactical considerations when planning a stroke. The results are validated in simulations and real-life experiments with an anthropomorphic robot playing pool against a human.

Abstract-This video presents a robot capable of playing pool on a normal sized pool table using two arms. For successfully completing this task several issues need to be addressed, including the perception of relevant environment information, planning of actions and finally an efficient execution. The video outlines how the robot accurately locates the pool table, the balls on the table and the cue and subsequently plans the next shot. In order to improve the stroke speed, an optimization algorithm for the arm configuration is described. Finally, it is shown how all these modules are integrated to achieve a working two-handed robotic pool play.

Abstract-Adjusting to new situations by changing the shape of a prerecorded trajectory is an important aspect for robot manipulation in a constrained environment. For being recognized as a distinctive trajectory, the goal of any trajectory modification is to keep local and global properties as similar as possible compared to the reference trajectory. This paper presents a framework that can alter the shape of a trajectory by defining the position of a set of sampling points while maintaining local properties in a least-squares manner. The method consists of a three-staged approach first modifying the global shape of the trajectory and subsequently taking local features into account. Inspired by mesh processing used for 3D surface editing, differential coordinates based on the discretized Laplacian operator are used for measuring and maintaining local trajectory properties when deforming the trajectory. Last, a post-processing step based on a relaxed "as-rigid-as-possible" principle allows local deformations and length modifications of the trajectory for a better tradeoff between preserving local and global properties. Experiments verifying the applicability of the proposed algorithm are conducted using a 7-DoF anthropomorphic arm following a previously recorded and modified trajectory.

Abstract-This paper discusses an online dynamic motion generation scheme for nonprehensile object manipulation by using a set of predefined motions and a trajectory deformation algorithm capable of incorporating positional and velocity boundary constraints. By creating optimal trajectories offline and deforming them online, computational complexity during execution is reduced considerably. As tight convex hulls of the deformed trajectories can be found, possible obstacles or workspace boundaries can be circumnavigated precisely without collision. The approach is verified through experiments on an inclined planar air-table for volleyball scenario using two 3-DoF robots.

Abstract-The planning and execution of real-world robotic tasks largely depend on the ability to generate feasible motions online in response to changing environment conditions or goals. A spline deformation method is able to modify a given trajectory so that it matches the new boundary conditions, e.g. on positions, velocities, accelerations, etc. At the same time, the deformed motion preserves velocity, acceleration, jerk or higher derivatives of motion profile of precalculated trajectory. The deformed motion possessing such properties can be expressed by translation of original trajectory and spline interpolation. This spline decomposition considerably reduces the computational complexity and allows the real-time execution. Formal feasibility guarantees are provided for the deformed trajectory and for the resulting torques. These guarantees are based on the special properties of Bernstein polynomials used for the deformation and on the structure of the chosen computed torque control scheme. The approach is experimentally evaluated in a number of planar volleyball experiments using 3-DoF robots and human participants.

The objective of this research is to push the frontiers in Automated Machine Learning, specifically targeting Deep Learning. We analyse ChaLearn's Automated Deep Learning challenge whose design features include: (i) Code submissions entirely blind-tested, on five classification problems during development, then ten others during final testing. (ii) Raw data from various modalities (image, video, text, speech, tabular data), formatted as tensors. (iii) Emphasis on "any-time learning" strategies by imposing fixed time/memory resources and using the Area under Learning curve as metric. (iv) Baselines provided, including "Baseline 3", combining top-ranked solutions of past rounds (AutoCV, AutoNLP, AutoSpeech,and AutoSeries). (v) No Deep Learning imposed. Principal findings: (1) The top two winners passed all final tests without failure, a significant step towards true automation. Their solutions were open-sourced.(2) Despite our effort to format all datasets uniformly to encourage generic solutions, the participants adopted specific workflows for each modality. (3) Any-time learning was addressed successfully, without sacrificing final performance. (4) Although some solutions improved over Baseline 3, it strongly influenced many. (5) Deep Learning solutions dominated, but Neural Architecture Search was impractical within the time budget imposed. Most solutions relied on fixed-architecture pre-trained networks, with fine-tuning. Ablation studies revealed the importance of meta-learning, ensembling, and efficient data loading, while data-augmentation is not critical.