No / After an observer adapts to a moving stimulus, texture within a stationary stimulus is perceived to drift in the opposite direction¿the traditional motion aftereffect (MAE). It has recently been shown that the perceived position of objects can be markedly influenced by motion adaptation [1] and [2]. In the present study, we examine the selectivity of positional shifts resulting from motion adaptation to stimulus attributes such as velocity, relative contrast, and relative spatial frequency. In addition, we ask whether spatial position can be modified in the absence of perceived motion. Results show that when adapting and test stimuli have collinear carrier gratings, the global position of the object shows a substantial shift in the direction of the illusory motion. When the carrier gratings of the adapting and test stimuli are orthogonal (a configuration in which no MAE is experienced), a global positional shift of similar magnitude is found. The illusory positional shift was found to be immune to changes in spatial frequency and to contrast between adapting and test stimuli¿manipulations that dramatically reduce the magnitude of the traditional MAE. The lack of sensitivity for stimulus characteristics other than direction of motion suggests that a specialized population of cortical neurones, which are insensitive to changes in a number of rudimentary visual attributes [3], may modulate positional representation in lower cortical areas.

No / We used biased random-dot dynamic test stimuli to measure the strength of the motion aftereffect (MAE) to evaluate the usefulness of this technique as a measure of motion adaptation strength. The stimuli consisted of noise dots whose individual directions were random and of signal dots moving in a unique direction. All dots moved at the same speed. For each condition, the nulling percentage (percentage of signal dots needed to perceptually null the MAE) was scaled with respect to the coherence threshold (percentage needed to perceive the coherent motion of signal dots without prior adaptation). The increase of these scaled values with the density of dots in the test stimulus suggests that MAE strength is underestimated when measured with low densities. We show that previous reports of high nulling percentages at slow speeds do not reflect strong MAEs, but are actually due to spatio-temporal aliasing, which dramatically increases coherence thresholds. We further show that MAE strength at slow speed increases with eccentricity. These findings are consistent with the idea that using this dynamic test stimulus preferentially reveals the adaptation of a population of high-speed motion units whose activity is independent of adapted low-speed motion units.

No / Until recently, it was widely believed that object position and object motion were represented independently in the visual cortex. However, several studies have shown that adaptation to motion produces substantial shifts in the perceived position of subsequently viewed stationary objects [[13]]. Two stages of motion adaptation have been proposed: an initial stage at the level of V1 and a secondary stage thought to be located in V5/MT [[4]]. Indeed, selective adaptation can be demonstrated at each of these levels of motion analysis [[5, 6]]. What remains unknown is which of these cortical sites are involved in modulating the positional representation of subsequently viewed objects. To answer this question directly, we disrupted cortical activity by using transcranial magnetic stimulation (TMS) immediately after motion adaptation. When TMS was delivered to V5/MT after motion adaptation, the perceived offset of the test stimulus was greatly reduced. In marked contrast, TMS of V1 had no effect on the changes that normally occur in perceived position after motion adaptation. This result demonstrates that the anatomical locus at which motion and positional information interact is area V5/MT rather than V1/V2.

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The use of robotics has recently seen significant growth in various domains such as unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid robots. However, one of the most important and essential capabilities required for long term autonomy, which is the ability to operate robustly and safely in real-world environments, in contrast to industrial and laboratory setup is largely missing. Designing robots that can operate reliably and efficiently in cluttered and changing environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e. robots with multiple actuators. On one hand, the dexterity offered by the kinematic redundancy allows the robot to perform dexterous manipulation tasks in complex environments, whereas on the other hand, such complex system also makes controlling and planning very challenging. To address such two interrelated problems, we exploit robot motion synthesis from three perspectives that feed into each other: end-pose planning, motion planning and motion adaptation. We propose several novel ideas in each of the three phases, using which we can efficiently synthesise dexterous manipulation motion for fixed-base robotic arms, mobile manipulators, as well as humanoid robots in cluttered and potentially changing environments. Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite for other modules such as motion planning, is an important and yet unsolved problem in robotics. Such information is often assumed given, or manually provided in practice, which significantly limiting high-level autonomy. In our research, by using novel data pre-processing and encoding techniques, we are able to efficiently search for collision-free end-poses in challenging scenarios in the presence of uneven terrains. After having found the end-poses, the motion planning module can proceed. Although motion planning has been claimed as well studied, we find that existing algorithms are still unreliable for robust and safe operations in real-world applications, especially when the environment is cluttered and changing. We propose a novel resolution complete motion planning algorithm, namely the Hierarchical Dynamic Roadmap, that is able to generate collision-free motion trajectories for redundant robotic arms in extremely complicated environments where other methods would fail. While planning for fixed-base robotic arms is relatively less challenging, we also investigate into efficient motion planning algorithms for high DoF (30 - 40) humanoid robots, where an extra balance constraint needs to be taken into account. The result shows that our method is able to efficiently generate collision-free whole-body trajectories for different humanoid robots in complex environments, where other methods would require a much longer planning time. Both end-pose and motion planning algorithms compute solutions in static environments, and assume the environments stay static during execution. While human and most animals are incredibly good at handling environmental changes, the state-of-the-art robotics technology is far from being able to achieve such an ability. To address this issue, we propose a novel state space representation, the Distance Mesh space, in which the robot is able to remap the pre-planned motion in real-time and adapt to environmental changes during execution. By utilizing the proposed end-pose planning, motion planning and motion adaptation techniques, we obtain a robotic framework that significantly improves the level of autonomy. The proposed methods have been validated on various state-of-the-art robot platforms, such as UR5 (6-DoF fixed-base robotic arm), KUKA LWR (7-DoF fixed-base robotic arm), Baxter (14-DoF fixed-base bi-manual manipulator), Husky with Dual UR5 (15-DoF mobile bi-manual manipulator), PR2 (20-DoF mobile bi-manual manipulator), NASA Valkyrie (38-DoF humanoid) and many others, showing that our methods are truly applicable to solve high dimensional motion planning for practical problems.