ICRA 2021 论文列表

Input-output transmission ratio shifting mechanisms provide a variable transmission ratio, which effectively expands a speed-force operating range of actuators. Although it is the most effective solution to increase the performance of robotic systems, its application to compact robotic systems still remains a challenging issue due to its complexity and massive structure. In this paper, we introduce a twisted string actuation-based transmission module for compact robotic systems. The twisted string mechanism provides a simplified transmission design and a compact form factor of the transmission module. An automatic transmission shifting algorithm is proposed for effective and autonomous control strategies. The developed prototype is integrated into a robotic gripper/hand, and its performance is verified with grasping demonstrations.

Loop closure detection (LCD), which aims to deal with the drift emerging when robots travel around the route, plays a key role in a simultaneous localization and mapping system. Unlike most current methods which focus on seeking an appropriate representation of images, we propose a novel two-stage pipeline dominated by the estimation of spatial geometric relationship. When a query image occurs, we select candidates on-line according to the similarity of global semantic features in the first stage, and then conduct robust geometric confirmation to verify true loop-closing pairs in the second stage. To this end, a robust feature matching algorithm, termed as bidirectional manifold representation consensus (BMRC), is proposed. In particular, we utilize manifold representation to construct local neighborhood structures of feature points and formulate the matching problem into an optimization model, enabling linearithmic time complexity via a closed-form solution. Furthermore, we propose a dynamic place partition strategy based on BMRC to segment image streams with similar content into a place, which can mine more valid candidate frames, improving the recall rate of the whole system. Extensive experiments on several publicly available datasets reveal that BMRC has a good performance in the general feature matching task and the proposed pipeline outperforms the current state-of-the-art approaches in the LCD task.

A human-centered robot needs to reason about the cognitive limitation and potential irrationality of its human partner to achieve seamless interactions. This paper proposes an anytime game-theoretic planner that integrates iterative reasoning models, a partially observable Markov decision process, and chance-constrained Monte-Carlo belief tree search for robot behavioral planning. Our planner enables a robot to safely and actively reason about its human partner’s latent cognitive states (bounded intelligence and irrationality) in real-time to maximize its utility better. We validate our approach in an autonomous driving domain where our behavioral planner and a low-level motion controller hierarchically control an autonomous car to negotiate traffic merges. Simulations and user studies are conducted to show our planner’s effectiveness.

The Correlated Team Orienteering Problem (CTOP) is a routing problem where the objective is to determine a set of routes that maximizes the summation of collected rewards in the environment while respecting the vehicles’ budget. However, solutions to this problem usually consider static instances and may produce poor results in dynamic real-world scenarios. In this paper, we propose an approach to deal with the execution of missions planned as a CTOP instance, especially when vehicles of the team are prone to failure and may not complete their routes. The main contribution of this paper is a novel anytime heuristic that iteratively adapts the initial set of routes, allowing to increase the overall robustness of the mission and still collect the most profitable rewards. The methodology was thoroughly evaluated considering different scenarios and in all the cases was able to achieve comparable or better results in terms of reward than planning a new set of routes, however, spending considerably less time.

In this work, we consider the problem of planning paths for a team of autonomous unmanned aerial vehicles (UAVs) to assist search and rescue practitioners. To address the problem, we develop a fully integrated framework that includes information from all aspects of the search environment. We take into consideration lost person motion via a behavior-based predictive model, anticipated human searcher trajectories, as well as measurements from fixed field of view sensors on board UAVs. We use a metric of posterior risk as the optimization target as it is an indicator of improved situational awareness and the effectiveness of continuing search efforts. Monte Carlo simulations are presented to demonstrate the effectiveness of the proposed framework.

Continuum arms are more adaptable to their environments and inherently human-friendly compared to their rigid counterparts. Path planning of continuum arms is an active research area with many challenges. The hyper-redundancy of continuum arms, which renders them highly versatile, is their curse in path planning. This problem becomes even more challenging in dynamic environments in the presence of mobile obstacles. In this paper, we propose an anticipatory path planning approach for continuum arms in dynamic environments. Our approach is based on obstacle prediction coupled with temporal graphs to model the dynamic environment. We evaluate the proposed approach’s performance and compare it to prevailing path planning approaches for continuum arms in dynamic environments.

Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other’s actions, and anticipate their movements. This paper presents Stochastic Process Anticipatory Navigation (SPAN), a framework that enables nonholonomic robots to navigate in environments with crowds, while anticipating and accounting for the motion patterns of pedestrians. To this end, we learn a predictive model to predict continuous-time stochastic processes to model future movement of pedestrians. Anticipated pedestrian positions are used to conduct chance constrained collision-checking, and are incorporated into a time-to-collision control problem. An occupancy map is also integrated to allow for probabilistic collision-checking with static obstacles. We demonstrate the capability of SPAN in crowded simulation environments, as well as with a real-world pedestrian dataset.

Designing adaptable control laws that can transfer between different robots is a challenge because of kinematic and dynamic differences, as well as in scenarios where external sensors are used. In this work, we empirically investigate a neural networks ability to approximate the Jacobian matrix for an application in Cartesian control schemes. Specifically, we are interested in approximating the kinematic Jacobian, which arises from kinematic equations mapping a manipulator’s joint angles to the end-effector’s location. We propose two different approaches to learn the kinematic Jacobian. The first method arises from visual servoing where we learn the kinematic Jacobian as an approximate linear system of equations from the k-nearest neighbors for a desired joint configuration. The second, motivated by forward models in machine learning, learns the kinematic behavior directly and calculates the Jacobian by differentiating the learned neural kinematics model. Simulation experimental results show that both methods achieve better performance than alternative data-driven methods for control, provide closer approximations to the proper kinematics Jacobian matrix, and on average produce better-conditioned Jacobian matrices. Real-world experiments were conducted on a Kinova Gen-3 lightweight robotic manipulator, which includes an uncalibrated visual servoing experiment, a practical application of our methods, as well as a 7-DOF point-to-point task highlighting that our methods are applicable on real robotic manipulators.

Predictive human models often need to adapt their parameters online from human data. This raises previously ignored safety-related questions for robots relying on these models such as what the model could learn online and how quickly could it learn it. For instance, when will the robot have a confident estimate in a nearby human’s goal? Or, what parameter initializations guarantee that the robot can learn the human’s preferences in a finite number of observations? To answer such analysis questions, our key idea is to model the robot’s learning algorithm as a dynamical system where the state is the current model parameter estimate and the control is the human data the robot observes. This enables us to leverage tools from reachability analysis and optimal control to compute the set of hypotheses the robot could learn in finite time, as well as the worst and best-case time it takes to learn them. We demonstrate the utility of our analysis tool in four human-robot domains, including autonomous driving and indoor navigation.

This paper offers an explanation of why humans can effortlessly grasp objects from a pile. We identified a regularity in objects’ motion when pushed, namely, an object separates and stabilizes in front of the pusher. We devise an open-loop grasping strategy leveraging this regularity in piles of nearly identical objects. Our real robot robustly grasps round objects beside a wall with success rates between 95% and 100% without visual or tactile feedback. We analyze our grasping strategy extensively both in real-world and simulated experiments. We observe that object roundness improves grasping and the motion pattern also manifests in small piles beside a wall. Our qualitative simulation can approximate the real robot’s grasping behavior, and we apply open-loop grasping in an warehouse pick-and-place application.

We consider the problem of rearranging objects in a cluttered and confined space using a robotic manipulator. The goal is to retrieve a target object from the clutter where the target is occluded by other objects. In situations where overhand grasps are not allowed, the robot needs to remove some objects to make the target accessible. In the course of removing the objects, the robot also needs to determine the locations to place the removed objects. If the robot can access enough empty spaces around or inside the clutter, the placement of the objects is trivially simple. If empty spaces are scarce, placing objects should be done in a principled way as an incorrect placement would deplete the empty spaces quickly.In this work, we propose a method that solves the problems of what and where to relocate objects inside the clutter to retrieve the target. Previously, there have been several efficient methods proposed that deal with each of the what and where to relocate problems separately. We solve the problems together using a graph structure constructed from an object configuration. Also, the method runs fast so scalable in the number of objects. Compared to a state-of-the-art method, our method reduces task and motion planning time up to 74.9% (at least 56.7%) and has a higher success rate under a short time limit for planning, which is 3 minutes.

Robots are nowadays faced with the challenge of handling deformable objects in industrial operations. In particular, the problem of shape control, which aims at giving a specific deformation state to an object, has gained interest recently in the research community. Among the proposed solutions, approaches based on finite elements proved accurate and reliable but also complex and computationally-intensiveIn order to mitigate these drawbacks, we propose a scheme for shape control that does not require to run a real-time simulation or to solve an implicit optimization problem for computing the control outputs. It is based on a partition of the nodal coordinates that allows deriving a control law directly from tangent stiffness matrices. This formulation is also coupled with the introduction of reduced finite element models. Simulation and experimental results in the context of linear deformable object manipulation demonstrate the interest of the proposed approach.

In this paper, we present a diabolo model that can be used for training agents in simulation to play diabolo, as well as running it on a real dual robot arm system. We first derive an analytical model of the diabolo-string system and compare its accuracy using data recorded via motion capture, which we release as a public dataset of skilled play with diabolos of different dynamics. We show that our model outperforms a deep-learning-based predictor, both in terms of precision and physically consistent behavior. Next, we describe a method based on optimal control to generate robot trajectories that produce the desired diabolo trajectory, as well as a system to transform higher-level actions into robot motions. Finally, we test our method on a real robot system playing the diabolo, and throw it to and catch it from a human player.

Heterogeneous multi-robot systems are advantageous for operations in unknown environments because functionally specialised robots can gather environmental information, while others perform tasks. We de ne this decomposition as the scout–task robot architecture and show how it avoids the need to explicitly balance exploration and exploitation by permitting the system to do both simultaneously. The challenge is to guide exploration in a way that improves overall performance for time-limited tasks. We derive a novel upper confidence bound for simultaneous exploration and exploitation based on mutual information and present a general solution for scout–task coordination using decentralised Monte Carlo tree search. We evaluate the performance of our algorithms in a multi-drone surveillance scenario in which scout robots are equipped with low-resolution, long-range sensors and task robots capture detailed information using short-range sensors. The results address a new class of coordination problem for heterogeneous teams that has many practical applications.

We introduce an underactuated differential-based robot gripper able to perform self-adaptive grasping with passive disturbance rejection. The gripper utilises three car differential systems to achieve self-adaptiveness with a single actuator: a base differential for distributing power from the motor to the fingers, and two independent finger differentials for controlling the proximal and distal joints. Linear and torsional springs are cleverly added to these differentials to allow the return of the fingers and the gripper-object system to equilibrium, thus enabling the gripper rejecting unexpected external forces applied to the fingers after securing a grasp. This novel design provides passive disturbance rejection without implementing complicated control systems and is the main contribution of this paper. Moreover, the differentials allow the gripper to perform not only self-adaptive power grasp but also precision grasp, provide it with a large force transmission efficiency, and facilitate the prediction of grasping position. We analyse the static model of the introduced differential system and evaluate the gripper design via four sets of experiments. Numerical and empirical results clearly demonstrate the viability of the proposed grasper.

Planar mechanisms dominate modern designs of legged robots with remote actuator placement for robust agility in ground mobility. This paper presents a novel design of robotic leg modules using the Bennett linkage, driven by two coaxially arranged quasi-direct actuators capable of omnidirectional ground locomotion. The Bennett linkage belongs to a family of overconstrained linkages with three-dimensional spatial motion and unparalleled joint axes. We present the first work regarding the design, modeling, and optimization of the Bennett leg module, enabling lateral locomotion, like the crabs, that was not capable with robotic legs designed with common planar mechanisms. We further explored the concept of overconstrained robots, which is a class of advanced robots based on the design reconfiguration of the Bennett leg modules, serving as a potential direction for future research.

In this paper, we propose a novel bilateral teleoperation architecture that allows to optimally render the remote interaction force at the local side while guaranteeing a robustly stable behaviour. Stability is guaranteed by ensuring a proper energy exchange between the local and the remote sides. Desired performance is obtained by optimizing the way energy is exploited for generating the behaviour at each side. The effectiveness of the proposed architecture is experimentally validated on a torque-controlled manipulator and in a surgical scenario, using the da Vinci® Research Kit (dVRK).

In Human-Robot Collaboration, the robot operates in a highly dynamic environment. Thus, it is pivotal to guarantee the robust stability of the system during the interaction but also a high flexibility of the robot behavior in order to ensure safety and reactivity to the variable conditions of the collaborative scenario.In this paper we propose a control architecture capable of maximizing the flexibility of the robot while guaranteeing a stable behavior when physically interacting with the environment. This is achieved by combining an energy tank based variable admittance architecture with control barrier functions. The proposed architecture is experimentally validated on a collaborative robot.

This paper presents a new open-source mechanical design of a 6-DOF anthropomorphic ALARIS robotic hand that can serve as a low-cost design platform for further customization and utilization for research and educational purposes. The presented hand design employs linkage-based three-phalange finger and two-phalange adaptive thumb designs with non-backdrivable worm-and-rack transmission mechanisms. Combination of design improvements and solutions, discussed in the paper, are implemented in a functional robotic hand prototype with powerful grasping capabilities, which utilizes off-the-shelf inexpensive components and 3D printing technology ensuring the hand low manufacturing cost and replicability. The open-source mechanical design of the presented ALARIS robotic hand is freely available for downloading from the authors’ research lab web-site https://www.alaris.kz and https://github.com/alarisnu/alaris_hand.