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 to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.
University at Albany, SUNY（奥尔巴尼大学（纽约州立大学））；University of Richmond（里士满大学）
Many state-of-art robotics applications require fast and efficient motion planning algorithms. Existing motion planning methods become less effective as the dimensionality of the robot and its workspace increases, especially the computational cost of collision detection routines. In this work, we present a framework to address the cost of expensive primitive operations in sampling-based motion planning. This framework determines the validity of a sample robot configuration through a novel combination of a Contractive AutoEncoder (CAE), which captures a occupancy grids representation of the rob…
In this paper, we propose a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base system to find valid end-poses in complex and dynamically changing environments in real-time. End-pose planning for valid stance pose and collision-free configuration is an essential problem for humanoid applications, such as providing goal states for walking and motion planners. However, this is non-trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed iDRM customizes the robot-to-workspace occupation list and uses an online …
The control of a robot for manipulation tasks generally relies on object detection and pose estimation. An attractive alternative is to learn control policies directly from raw input data. However, this approach is time-consuming and expensive since learning the policy requires many trials with robot actions in the physical environment. To reduce the training cost, the policy can be learned in simulation with a large set of synthetic images. The limit of this approach is the domain gap between the simulation and the robot workspace. In this paper, we propose to learn a policy for robot reachin…
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…
This paper studies motion planning of a mobile robot under uncertainty. The control objective is to synthesize a {finite-memory} control policy, such that a high-level task specified as a Linear Temporal Logic (LTL) formula is satisfied with a desired high probability. Uncertainty is considered in the workspace properties, robot actions, and task outcomes, giving rise to a Markov Decision Process (MDP) that models the proposed system. Different from most existing methods, we consider cost optimization both in the prefix and suffix of the system trajectory. We also analyze the potential trade-o…
A new type of parallel robot mechanism with an extendable structure is presented, and its kinematic properties and design parameters are analyzed. The Triple Scissor Extender (TSE) is a 6 Degree-Of-Freedom robotic mechanism for reaching high ceilings and positioning an end effector. Three scissor mechanisms are arranged in parallel, with the bottom ends coupled to linear slides, and the top vertex attached to an end effector plate. Arbitrary positions and orientations of the end effector can be achieved through the coordinated motion of the six linear actuators located at the base. By changing…
Proprioceptive-visual correspondence enables self-other distinction in humanoid robots
Eastern Institute of Technology, Ningbo（宁波东方理工大学）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；East China Normal University（华东师范大学）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）
Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；Tampere University（坦佩雷大学）；Aalto University（阿尔托大学）
We propose a closed-loop, multi-instance control algorithm for visually guided reaching based on novel learning principles. A control Lyapunov function methodology is used to design a reaching action for a complex multi-instance task in the case where full state information (poses of all potential reaching points) is available. The proposed algorithm uses monocular vision and manipulator joint angles as the input to a deep convolution neural network to predict the value of the control Lyapunov function (cLf) and corresponding velocity control. The resulting network output is used in real-time …
Czech Technical University in Prague（布拉格捷克技术大学）；Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克技术大学电气工程学院）
The need to guarantee safety of collaborative robots limits their performance, in particular, their speed and hence cycle time. The standard ISO/TS 15066 defines the Power and Force Limiting operation mode and prescribes force thresholds that a moving robot is allowed to exert on human body parts during impact, along with a simple formula to obtain maximum allowed speed of the robot in the whole workspace. In this work, we measure the forces exerted by two collaborative manipulators (UR10e and KUKA LBR iiwa) moving downward against an impact measuring device. First, we empirically show that th…
Towards Learning a Generalizable 3D Scene Representation from 2D Observations
University of Hamburg - Department of Informatics（汉堡大学信息学院）；ZAL Center of Applied Aeronautical Research（应用航空研究中心）；Hamburger Informatik Technologie-Center e.V. (HITeC)（汉堡信息科技中心（HITeC））
Proactively perceiving others' intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments. This work proposes a first step towards embedding this skill in support robots for search and rescue missions. Predicting the responders' intentions, indeed, will enable exploration approaches which will identify and prioritise areas that are more relevant for the responder and, thus, for the task, leading to the development of safer, more robust and efficient joint exploration strategies. More specifically, this paper presents an active intention recognition pa…
Reaching tasks with random targets and obstacles is a challenging task for robotic manipulators. In this study, we propose a novel model-free reinforcement learning approach based on proximal policy optimization (PPO) for training a deep policy to map the task space to the joint space of a 6-DoF manipulator. To facilitate the training process in a large workspace, we develop an efficient representation of environmental inputs and outputs. The calculation of the distance between obstacles and manipulator links is incorporated into the state representation using a geometry-based method. Addition…
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…
Mobile Manipulation (MM) systems are ideal candidates for taking up the role of a personal assistant in unstructured real-world environments. Among other challenges, MM requires effective coordination of the robot's embodiments for executing tasks that require both mobility and manipulation. Reinforcement Learning (RL) holds the promise of endowing robots with adaptive behaviors, but most methods require prohibitively large amounts of data for learning a useful control policy. In this work, we study the integration of robotic reachability priors in actor-critic RL methods for accelerating the …
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education（教育部机制理论与装备设计重点实验室）；Tianjin University（天津大学）；Department of Computer Science（计算机科学系）；Loughborough University（洛厄尔大学）；University of Cambridge（剑桥大学）
When robots share the same workspace with other intelligent agents (e.g., other robots or humans), they must be able to reason about the behaviors of their neighboring agents while accomplishing the designated tasks. In practice, frequently, agents do not exhibit absolutely rational behavior due to their limited computational resources. Thus, predicting the optimal agent behaviors is undesirable (because it demands prohibitive computational resources) and undesirable (because the prediction may be wrong). Motivated by this observation, we remove the assumption of perfectly rational agents and …
Deep reinforcement learning in continuous domains focuses on learning control policies that map states to distributions over actions that ideally concentrate on the optimal choices in each step. In multi-agent navigation problems, the optimal actions depend heavily on the agents' density. Their interaction patterns grow exponentially with respect to such density, making it hard for learning-based methods to generalize. We propose to switch the learning objectives from predicting the optimal actions to predicting sets of admissible actions, which we call control admissibility models (CAMs), suc…
Providing an accurate and efficient assessment of operative difficulty is important for designing robot-assisted teleoperation interfaces that are easy and natural for human operators to use. In this paper, we aim to develop a data-driven approach to numerically characterize the operative difficulty demand of complex teleoperation. In effort to provide an entirely task-independent assessment, we consider using only data collected from the human user including: (1) physiological response, and (2) movement kinematics. By leveraging an unsupervised domain adaptation technique, our approach learns…
