Sliding states of a soft-colloid cluster crystal: Cluster versus single-particle hopping

We study a two-dimensional model for interacting colloidal particles which displays spontaneous clustering. Within this model we investigate the competition between the pinning to a periodic corrugation potential, and a sideways constant pulling force which would promote a sliding state. For a few sample particle densities and amplitudes of the periodic corrugation potential we investigate the depinning from the statically pinned to the dynamically sliding regime. This sliding state exhibits the competition between a dynamics where entire clusters are pulled from a minimum to the next and a dynamics where single colloids or smaller groups leave a cluster and move across the corrugation energy barrier to join the next cluster downstream in the force direction. Both kinds of sliding states can occur either coherently across the entire sample, or asynchronously: the two regimes result in different average mobilities. Finite temperature tends to destroy separate sliding regimes, generating a smoother dependence of the mobility on the driving force.Comment: 14 pages, 19 figure

Learning Expressive Quadrupedal Locomotion Guided by Kinematic Trajectory Generator

Biological quadrupedal systems exhibit a wider range of locomotion skills. In Robotics, quadrupedal systems only exhibit a limited range of locomotion skills. They can be very robust for a single locomotion task, and state-of-the-art algorithms have been designed for walking gaits or use individual policies trained for a single skill. This thesis aimed to study the design of an expressive locomotion controller (different locomotion skills in one policy) for a quadrupedal robot. Different approaches based on Deep Reinforcement Learning have been studied for their recent successes in Robotics and Computer animation. A reference-free and a reference-based approach using solely reward shaping, i.e. specification of the motion through the reward, have been implemented. They produced walking gaits in simulation. Yet, the motions produced by the reference-based approach had limited footstep height and balance issues. The reference-free approach had higher footsteps and fewer base oscillations. Yet, both approaches are hard to adapt when it comes to expressiveness since the motion specification is solely done through reward shaping, which is not intuitive. Finally, inspired by works in computer animation and robotics, an approach based on motion clips for motion specification and general motion tracking has been implemented and produced more natural motions in simulation, i.e. higher footsteps, bigger strides, more base stability hard to generate using reward shaping.M.S

Model of Coordination Flow in Remote Collaborative Interaction

© 2015 IEEEWe present an information-theoretic approach for modelling coordination in human-human interaction and measuring coordination flows in a remote collaborative tracking task. Building on Shannon's mutual information, coordination flow measures, for stochastic collaborative systems, how much influence, the environment has on the joint control of collaborating parties. We demonstrate the application of the approach on interactive human data recorded in a user study and reveal the amount of effort required for creating rigorous models. Our initial results suggest the potential coordination flow has - as an objective, task-independent measure - in supporting designers of human collaborative systems and in providing better theoretical foundations for the science of Human-Computer Interaction

A Two-part Transformer Network for Controllable Motion Synthesis

Although part-based motion synthesis networks have been investigated to reduce the complexity of modeling heterogeneous human motions, their computational cost remains prohibitive in interactive applications. To this end, we propose a novel two-part transformer network that aims to achieve high-quality, controllable motion synthesis results in real-time. Our network separates the skeleton into the upper and lower body parts, reducing the expensive cross-part fusion operations, and models the motions of each part separately through two streams of auto-regressive modules formed by multi-head attention layers. However, such a design might not sufficiently capture the correlations between the parts. We thus intentionally let the two parts share the features of the root joint and design a consistency loss to penalize the difference in the estimated root features and motions by these two auto-regressive modules, significantly improving the quality of synthesized motions. After training on our motion dataset, our network can synthesize a wide range of heterogeneous motions, like cartwheels and twists. Experimental and user study results demonstrate that our network is superior to state-of-the-art human motion synthesis networks in the quality of generated motions.Comment: 16 pages, 26 figure

Motor Control For Human Jumping To A Target

Investigating how humans perform dynamic movements is important for applications such as movement rehabilitation, sports training, humanoid robot design and control, and human-robot interaction. There are several hypotheses as to how humans perform dynamic movements based on movement variability, task optimization, and motor learning concepts. This thesis develops a methodology for analyzing dynamic movements, determining what factors are crucial to task success, and understanding the motor learning process. The jumping to a target movement was chosen as the exemplar motion for investigating human dynamic motor control because of the following reasons: the movement difficulty can be scaled to a person's physical characteristics and ability; jumping to a target is a movement that many people can perform but few have practiced, making it a good candidate for investigating motor learning; jumping to target has a clear metric for success, enabling novice-expert classification of participants based on objective task performance. Additionally, existing human jumping research has focused primarily on maximum height vertical jumping or maximum distance long jumping. This thesis is the first known work to investigate the kinematics and motor control of the standing broad jump to a target. An experiment was conducted to collect motion capture data of 22 participants (ages 19-34 years, 9 females and 13 males), each performing 12 jumps to three specified targets of various distances. These motion capture data were used with Extended Kalman Filter pose estimation to extract the kinematic joint trajectories of each jump, and the center of mass (CoM) trajectories were then computed. Analysis of these trajectories then proceeded in two stages. A kinematic trajectory analysis was performed to identify trends between the jumping trajectories and jump success. The identified trends, and other information found in the literature, were used to generate hypotheses for using a sliding window Inverse Optimal Control (IOC) approach for identifying optimized motor control tasks. The findings from the kinematic trajectory analysis of jumping motion trajectories suggest a strong relation between the jumper controlling the velocity of their CoM at takeoff and the success of the jump. The angle and magnitude of the takeoff velocity must be matched to generate an appropriate ballistic trajectory to reach the desired target. At landing, the jumper can use their foot placement pose to correct for inaccuracy in their takeoff velocity and CoM trajectory to still land on the target. Novice jumpers demonstrated more consistent CoM takeoff velocities as they performed more jumps, however it was less likely that their foot placement control improved noticeably during the study. Expert jumpers were observed to control their foot placement pose more effectively, therefore making higher jumping success rates possible even when the variability of their CoM takeoff velocity was greater than some novice jumpers. A sliding window IOC approach was used to estimate what motor control tasks jumpers optimize throughout the movement. The cost terms of the objective function were designed based on jumping-specific control tasks and criteria relevant to general human motion. The recovered IOC cost term weights were averaged over different sets of jump features. Changes in average cost term weights were observed relative to jump grade, target distance, and jump performance. Experts were observed to optimize CoM forward velocity before takeoff more than novice jumpers, who optimized CoM height more. As novice jumpers improved their success rate during the experiment, their motor control behavior more closely resembled that of experts. The IOC approach demonstrates evidence for a repeatable, general optimal motor control method for jumping to a target. Parallels were also drawn between the kinematic trajectory results and IOC motor control task results. Optimizing for the CoM velocity control task before takeoff and toe velocity control task prior to landing, as identified in the IOC results, can be related to controlling takeoff velocity and foot placement pose respectively, as observed in the kinematic analysis. Finally, the IOC sliding window approach was used alongside unsupervised clustering techniques to identify four jump styles into which experiment participants could be categorized into. All style groups included novice and expert jumpers, and were independent of jump success or motor learning, suggesting there are multiple general motor control patterns that can be used for successfully jumping to a target. This analysis framework can be extended to analyzing jumping motions in varied environment conditions, or be used to define the motor control methods of other dynamic human motions