Online Planning for Autonomous Running Jumps Over Obstacles in High-Speed Quadrupeds

This paper presents a new framework for the generation of high-speed running jumps to clear terrain obstacles in quadrupedal robots. Our methods enable the quadruped to autonomously jump over obstacles up to 40 cm in height within a single control framework. Specifically, we propose new control system components, layered on top of a low-level running controller, which actively modify the approach and select stance force profiles as required to clear a sensed obstacle. The approach controller enables the quadruped to end in a preferable state relative to the obstacle just before the jump. This multi-step gait planning is formulated as a multiple-horizon model predictive control problem and solved at each step through quadratic programming. Ground reaction force profiles to execute the running jump are selected through constrained nonlinear optimization on a simplified model of the robot that possesses polynomial dynamics. Exploiting the simplified structure of these dynamics, the presented method greatly accelerates the computation of otherwise costly function and constraint evaluations that are required during optimization. With these considerations, the new algorithms allow for online planning that is critical for reliable response to unexpected situations. Experimental results, for a stand-alone quadruped with on-board power and computation, show the viability of this approach, and represent important steps towards broader dynamic maneuverability in experimental machines.United States. Defense Advanced Research Projects Agency. Maximum Mobility and Manipulation (M3) ProgramKorean Agency for Defense Development (Contract UD1400731D

Quadruped Bounding Control with Variable Duty Cycle via Vertical Impulse Scaling

This paper introduces a bounding gait control algorithm that allows a successful implementation of duty cycle modulation in the MIT Cheetah 2. Instead of controlling leg stiffness to emulate a ‘springy leg’ inspired from the Spring-Loaded-Inverted-Pendulum (SLIP) model, the algorithm prescribes vertical impulse by generating scaled ground reaction forces at each step to achieve the desired stance and total stride duration. Therefore, we can control the duty cycle: the percentage of the stance phase over the entire cycle. By prescribing the required vertical impulse of the ground reaction force at each step, the algorithm can adapt to variable duty cycles attributed to variations in running speed. Following linear momentum conservation law, in order to achieve a limit-cycle gait, the sum of all vertical ground reaction forces must match vertical momentum created by gravity during a cycle. In addition, we added a virtual compliance control in the vertical direction to enhance stability. The stiffness of the virtual compliance is selected based on the eigenvalue analysis of the linearized Poincare map and the chosen stiffness is 700 N/m, which corresponds to around 12% of the stiffness used in the previous trotting experiments of the MIT Cheetah, where the ground reaction forces are purely caused by the impedance controller with equilibrium point trajectories. This indicates that the virtual compliance control does not significantly contributes to generating ground reaction forces, but to stability. The experimental results show that the algorithm successfully prescribes the duty cycle for stable bounding gaits. This new approach can shed a light on variable speed running control algorithm.United States. Defense Advanced Research Projects Agency (M3 Program

Multipar-T: Multiparty-Transformer for Capturing Contingent Behaviors in Group Conversations

As we move closer to real-world AI systems, AI agents must be able to deal with multiparty (group) conversations. Recognizing and interpreting multiparty behaviors is challenging, as the system must recognize individual behavioral cues, deal with the complexity of multiple streams of data from multiple people, and recognize the subtle contingent social exchanges that take place amongst group members. To tackle this challenge, we propose the Multiparty-Transformer (Multipar-T), a transformer model for multiparty behavior modeling. The core component of our proposed approach is the Crossperson Attention, which is specifically designed to detect contingent behavior between pairs of people. We verify the effectiveness of Multipar-T on a publicly available video-based group engagement detection benchmark, where it outperforms state-of-the-art approaches in average F-1 scores by 5.2% and individual class F-1 scores by up to 10.0%. Through qualitative analysis, we show that our Crossperson Attention module is able to discover contingent behavior.Comment: 7 pages, 4 figures, IJCA

Providing tablets as collaborative task workspace for human-robot interaction

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 8th ACM/IEEE International Conference on Human-robot Interaction, HRI '13, Tokyo, Japan, March 3-6, 2013.In a recent conference on assistive technology in special education and rehabilitation, over 54 percentage of the sessions were directly or indirectly involved with tablets. Following this trend, many traditional assistive technologies are now transitioning from standalone devices into apps on mobile devices. As such, this paper follows this trend by discussing transforming a tablet into an HRI research platform where our robotic system engages the user in social interaction by learning how to operate a given app (task) using guidance from the user. The objective is to engage the robot within the context of the user's task by understanding the task's underlying rules and structures. An overview of the HRI toolkit is presented and a knowledge-based approach in modeling a task is discussed where previously learned cases are reused to solve a new problem

Recent Updates on Acquired Hepatocerebral Degeneration

Background: Acquired hepatocerebral degeneration (AHD) refers to a chronic neurological syndrome in patients with advanced hepatobiliary diseases. This comprehensive review focuses on the pathomechanism and neuroimaging findings in AHD. Methods: A PubMed search was performed using the terms “acquired hepatocerebral degeneration,” “chronic hepatocerebral degeneration,” “Non-Wilsonian hepatocerebral degeneration,” “cirrhosis-related parkinsonism,” and “manganese and liver disease.” Results: Multiple mechanisms involving the accumulation of toxic substances such as ammonia or manganese and neuroinflammation may lead to widespread neurodegeneration in AHD. Clinical characteristics include movement disorders, mainly parkinsonism and ataxia-plus syndrome, as well as cognitive impairment with psychiatric features. Neuroimaging studies of AHD with parkinsonism show hyperintensity in the bilateral globus pallidus on T1-weighted magnetic resonance images, whereas molecular imaging of the presynaptic dopaminergic system shows variable findings. Ataxia-plus syndrome in AHD may demonstrate high-signal lesions in the middle cerebellar peduncles on T2-weighted images. Discussion: Future studies are needed to elucidate the exact pathomechanism and neuroimaging findings of this heterogeneous syndrome

Metal/graphene sheets as p-type transparent conducting electrodes in GaN light emitting diodes

We demonstrate the use of graphene based transparent sheets as a p-type current spreading layer in GaN light emitting diodes (LEDs). Very thin Ni/Au was inserted between graphene and p-type GaN to reduce contact resistance, which reduced contact resistance from similar to 5.5 to similar to 0.6 Omega/ cm(2), with no critical optical loss. As a result, LEDs with metal-graphene provided current spreading and injection into the p-type GaN layer, enabling three times enhanced electroluminescent intensity compared with those with graphene alone. We confirmed very strong blue light emission in a large area of the metal-graphene layer by analyzing image brightness.open281