Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human movements. However, they are often not robust and their generalisation is limited. This work proposed a hierarchical control architecture for robot manipulators and provided capabilities of reproducing human-like motions during unknown interaction dynamics. Our results show that the reproduced end-effector trajectories can preserve the main characteristics of the initial human motion recorded via a motion capture system, and are robust against external perturbations. The data indicate that some detailed movements are hard to reproduce due to the physical limits of the hardware that cannot reach the same velocity recorded in human movements. Nevertheless, these technical problems can be addressed by using better hardware and our proposed algorithms can still be applied to produce imitated motions.
Warwick Manufacturing Group, University of Warwick（华威大学华威制造集团）；University of Warwick（华威大学）
Training agents to autonomously learn how to use anthropomorphic robotic hands has the potential to lead to systems capable of performing a multitude of complex manipulation tasks in unstructured and uncertain environments. In this work, we first introduce a suite of challenging simulated manipulation tasks that current reinforcement learning and trajectory optimisation techniques find difficult. These include environments where two simulated hands have to pass or throw objects between each other, as well as an environment where the agent must learn to spin a long pen between its fingers. We t…
Robotic teleoperation will allow us to perform complex manipulation tasks in dangerous or remote environments, such as needed for planetary exploration or nuclear decommissioning. This work proposes a novel telemanipulation architecture using a passive Fractal Impedance Controller (FIC), which does not depend upon an active viscous component for stability guarantees. Compared to a traditional impedance controller in ideal conditions (no delays and maximum communication bandwidth), our proposed method yields higher transparency in interaction and demonstrates superior dexterity and capability i…
Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics
Institute for Perception, Action, and Behaviour, School of Informatics, University of Edinburgh（爱丁堡大学信息学院感知、行动与行为研究所）；University College London（伦敦大学学院）；University of Sussex（萨塞克斯大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Université de Lorraine（洛林大学）
Hunan University（湖南大学）；Bielefeld University（比勒费尔德大学）；University of the West of England（西英格兰大学）
In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multi-fingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It…
The mechanism behind the generation of human movements is of great interest in many fields (e.g. robotics and neuroscience) to improve therapies and technologies. Optimal Feedback Control (OFC) and Passive Motion Paradigm (PMP) are currently two leading theories capable of effectively producing human-like motions, but they require solving nonlinear inverse problems to find a solution. The main benefit of using PMP is the possibility of generating path-independent movements consistent with the stereotypical behaviour observed in humans, while the equivalent OFC formulation is path-dependent. Ou…
The utility of Human-in-the-loop telerobotic systems (HiLTS) is driven in part by the quality of feedback it can provide to the operator. While the dynamic interaction between robot and environment can often be sensed or modeled, the dynamic coupling of the human-robot interface is often overlooked. Enabling dexterous manipulation through HiLTS however, will require careful consideration of human haptic perception as it relates to the human's changing limb impedance at the human-robot interface. In this manuscript, we present results from a stiffness perception task run on a simple 1-DoF rotat…
A Learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand
DLR Institute of Robotics & Mechatronics（德国宇航中心机器人与机电一体化研究所）；Technical University of Munich（慕尼黑工业大学）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
Istituto Italiano di Tecnologia（意大利理工学院）；Universita di Pisa（比萨大学）
Teleoperation is a widely adopted strategy to control robotic manipulators executing complex tasks that require highly dexterous movements and critical high-level intelligence. Classical teleoperation schemes are based on either joystick control, or on more intuitive interfaces which map directly the user arm motions into one robot arm's motions. These approaches have limits when the execution of a given task requires reconfigurable multiple robotic arm systems. Indeed, the simultaneous teleoperation of two or more robot arms could extend the workspace of the manipulation cell, or increase its…
University of Edinburgh（爱丁堡大学）；University of Oxford（牛津大学）
Path planning and collision avoidance are challenging in complex and highly variable environments due to the limited horizon of events. In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality. We propose a planning algorithm based on a globally stable passive controller that can plan smooth trajectories using limited computational resources in challenging environmental conditions. The architecture combines the recently proposed fractal impedance controller with elasti…
University of Sussex（萨塞克斯大学）；University of Edinburgh（爱丁堡大学）
The teleoperation of robots enables remote intervention in distant and dangerous tasks without putting the operator in harm's way. However, remote operation faces fundamental challenges due to limits in communication delays. The proposed work improves the performances of teleoperation architecture based on Fractal Impedance Controller (FIC) by integrating into the haptic teleoperation pipeline a postural optimisation that also accounts for the replica robots' physical limitations. This update improves dynamic interactions by trading off tracking accuracy to maintain the system within its power…
UK National Centre for Nuclear Robotics（英国国家核机器人中心）；University of Birmingham（伯明翰大学）；National Nuclear Laboratory（国家核实验室）；University of West England（西英格兰大学）
This paper presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape, but observes it from a noisy point-cloud camera. We consider the multi-objective optimisation problem of finding robot trajectories which maximise the robot's manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of \textit{rough} robotic cutting, \textit{…
