Minimum Jerk trajectories have been long thought to be the reference trajectories for human movements due to their impressive similarity with human movements. Nevertheless, minimum jerk trajectories are not the only choice for $C^\infty$ (i.e., smooth) functions. For example, harmonic trajectories are smooth functions that can be superimposed to describe the evolution of physical systems. This paper analyses the possibility that motor control plans using harmonic trajectories, will be experimentally observed to have a minimum jerk likeness due to control signals being transported through the Central Nervous System (CNS) and muscle-skeletal system. We tested our theory on a 3-link arm simulation using a recently developed planner that we reformulated into a motor control architecture, inspired by the passive motion paradigm. The arm performed 100 movements, reaching for each target defined by the clock experiment. We analysed the shape of the trajectory planned in the CNS and executed in the physical simulator. We observed that even under ideal conditions (i.e., absence of delays and noise) the executed trajectories are similar to a minimum jerk trajectory; thus, supporting the thesis that the human brain might plan harmonic trajectories.
Texas A&M University（德州农工大学）；Texas A&M University at Qatar（德州农工大学卡塔尔分校）
This paper proposes a computational approach for generation of reference path for upper-limb exoskeletons considering the scapulohumeral rhythms of the shoulder. The proposed method can be used in upper-limb exoskeletons with 3 Degrees of Freedom (DoF) in shoulder and 1 DoF in elbow, which are capable of supporting shoulder girdle. The developed computational method is based on Central Nervous System (CNS) governing rules. Existing computational reference generation methods are based on the assumption of fixed shoulder center during motions. This assumption can be considered valid for reaching…
Max-Planck-Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Worcester Polytechnic Institute（伍斯特理工学院）
To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation …
Monash University（莫纳什大学）；University of British Columbia（不列颠哥伦比亚大学）
The Minimum Jerk motion model has long been cited in literature for human point-to-point reaching motions in single-person tasks. While it has been demonstrated that applying minimum-jerk-like trajectories to robot reaching motions in the joint action task of human-robot handovers allows a robot giver to be perceived as more careful, safe, and skilled, it has not been verified whether human reaching motions in handovers follow the Minimum Jerk model. To experimentally test and verify motion models for human reaches in handovers, we examined human reaching motions in unconstrained handovers (wh…
In many human-in-the-loop robotic applications such as robot-assisted surgery and remote teleoperation, predicting the intended motion of the human operator may be useful for successful implementation of shared control, guidance virtual fixtures, and predictive control. Developing computational models of human movements is a critical foundation for such motion prediction frameworks. With this motivation, we present a computational framework for modeling reaching movements in the presence of obstacles. We propose a stochastic optimal control framework that consists of probabilistic collision av…
Learning Human Reaching Optimality Principles from Minimal Observation Inverse Reinforcement Learning
Machines in Motion Laboratory, New York University, USA（纽约大学运动机器实验室）；LAAS-CNRS, Université Paul Sabatier, CNRS, Toulouse, France（法国保罗·萨巴蒂埃大学LAAS-CNRS）；Image and Pervasive Access Laboratory (IPAL), CNRS-UMI, 2955, Singapore（新加坡图像与普及访问实验室（IPAL））；Artificial and Natural Intelligence Toulouse Institute (ANITI), Toulouse（图卢兹人工智能与自然智能研究所（ANITI））
This work focuses on closed-loop control based on proprioceptive feedback for a pneumatically-actuated soft wearable device aimed at future support of infant reaching tasks. The device comprises two soft pneumatic actuators (one textile-based and one silicone-casted) actively controlling two degrees-of-freedom per arm (shoulder adduction/abduction and elbow flexion/extension, respectively). Inertial measurement units (IMUs) attached to the wearable device provide real-time joint angle feedback. Device kinematics analysis is informed by anthropometric data from infants (arm lengths) reported in…
The aim of this paper is to study the reward based policy exploration problem in a supervised learning approach and enable robots to form complex movement trajectories in challenging reward settings and search spaces. For this, the experience of the robot, which can be bootstrapped from demonstrated trajectories, is used to train a novel Neural Processes-based deep network that samples from its latent space and generates the required trajectories given desired rewards. Our framework can generate progressively improved trajectories by sampling them from high reward landscapes, increasing the re…
Probabilistic Movement Primitives (ProMPs) are a widely used representation of movements for human-robot interaction. They also facilitate the factorization of temporal and spatial structure of movements. In this work we investigate a method to temporally align observations so that when learning ProMPs, information in the spatial structure of the observed motion is maximized while maintaining a smooth phase velocity. We apply the method on recordings of hand trajectories in a two-dimensional reaching task. A system for simultaneous recognition of movement and phase is proposed and performance …
Monash University（莫纳什大学）；University of British Columbia（不列颠哥伦比亚大学）；Australian National University（澳大利亚国立大学）；Deakin University（迪肯大学）；University of Victoria（维多利亚大学）
Robots must move legibly around people for safety reasons, especially for tasks where physical contact is possible. One such task is handovers, which requires implicit communication on where and when physical contact (object transfer) occurs. In this work, we study whether the trajectory model used by a robot during the reaching phase affects the subjective perceptions of receivers for robot-to-human handovers. We conducted a user study where 32 participants were handed over three objects with four trajectory models: three were versions of a minimum jerk trajectory, and one was an ellipse-fitt…
Toward Global Intent Inference for Human Motion by Inverse Reinforcement Learning
Machines in Motion Laboratory, New York University, USA（纽约大学运动机器实验室）；LAAS-CNRS, Université Paul Sabatier, CNRS, Toulouse, France（拉劳斯-CNRS，保罗·萨巴蒂埃大学，法国图卢兹）；Image and Pervasive Access Laboratory (IPAL), CNRS-UMI, Singapore（图像与普及访问实验室（IPAL），CNRS-UMI，新加坡）；Artificial and Natural Intelligence Toulouse Institute (ANITI), Toulouse（图卢兹人工智能与自然智能研究所（ANITI），图卢兹）
State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into integrating EMG with other signals, often in an ad hoc manner. In this paper, we are presenting a method for end-to-end training of a policy for human-in-the-loop robot grasping on real reaching trajectories. For this purpose we use Reinforcement Learning (RL) and Imitation Learning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation environment with real human trajectories that are augmented and selected us…
Beyond providing accurate movements, achieving smooth motion trajectories is a long-standing goal of robotics control theory for arms aiming to replicate natural human movements. Drawing inspiration from biological agents, whose reaching control networks effortlessly give rise to smooth and precise movements, can simplify these control objectives for robot arms. Neuromorphic processors, which mimic the brain's computational principles, are an ideal platform to approximate the accuracy and smoothness of biological controllers while maximizing their energy efficiency and robustness. However, the…
GCImOpt: Learning efficient goal-conditioned policies by imitating optimal trajectories
Department of Statistics Mathematics and Computer Science Public University of Navarre（统计数学与计算机科学系公共纳瓦拉大学）；Ensimag Institut National Polytechnique de Grenoble Université Grenoble Alpes（恩西马格国立技术学院格勒诺布尔大学）；Institute for Advanced Materials and Mathematics (InaMat 2 )（先进材料与数学研究所）
