High speed locomotion for a quadrupedal microrobot Baisch, Andrew T.; Ozcan, Onur; Goldberg, Benjamin ...
The International journal of robotics research,
07/2014, Letnik:
33, Številka:
8
Journal Article
Recenzirano
Research over the past several decades has elucidated some of the mechanisms behind high speed, highly efficient, and robust locomotion in insects such as cockroaches. Roboticists have used this ...information to create biologically inspired machines capable of running, jumping, and climbing robustly over a variety of terrains. To date, little work has been done to develop an at-scale insect-inspired robot capable of similar feats due to challenges in fabrication, actuation, and electronics integration for a centimeter-scale device. This paper addresses these challenges through the design, fabrication, and control of a 1.27 g walking robot, the Harvard Ambulatory MicroRobot (HAMR). The current design is manufactured using a method inspired by pop-up books that enables fast and repeatable assembly of the miniature walking robot. Methods to drive HAMR at low and high speeds are presented, resulting in speeds up to 0.44 m/s (10.1 body lengths per second) and the ability to maneuver and control the robot along desired trajectories.
Recent advances in soft robots have been achieved by using compliant materials and exploiting the advantages of the soft structural designs of living organisms. Living organisms (which have ...theoretically infinite degrees of freedom) are not only mechanically soft but are also capable of smooth harmonic motions, thanks to global coordination and the individual sensing and control of local tissues. Despite improvements in structural designs, few soft robot control frameworks for global object‐oriented behaviors are reported. Such a framework will require the use of multiple segments, with local sensing and independent control using coordinated policies. Here, a class of reinforcement learning based control frameworks for soft robots (with high degrees of freedom) is presented, and their ability to conduct global tasks is demonstrated. Coordinated control policies are formulated to control multiple segments with independently controllable embedded actuators, based on localized proprioceptive self‐sensing capabilities. The control frameworks are employed to develop soft physical robots. Demonstrations and experiments include the forward and backward locomotion of multichannel soft robotic flatworms. This approach is applicable to multifunctional, high degrees of freedom soft robots, as demonstrated by experiments with light‐sensitive locomotion.
The study introduces bioinspired control and design frameworks for high degrees of freedom soft robots, using reinforcement learning to conduct global tasks by formulating coordinated control policies for independently controllable multiple segments with embedded actuators (shape memory alloy wires), based on localized proprioceptive self‐sensing.
For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few works of literature are concerned with the convergence speed. In this paper, we ...developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate. Superior to the traditional methods, the information of parameter estimation error was properly integrated into the proposed identification algorithm, such that enhanced estimation performance was achieved. Besides, the Newton-Euler (NE) method was used to build the robot dynamic model, where a singular value decomposition-based model reduction method was designed to remedy the potential singularity problems of the NE regressor. Moreover, an interval excitation condition was employed to relax the requirement of persistent excitation condition for the kinematic estimation. By using the Lyapunov synthesis, explicit analysis of the convergence rate of the tracking errors and the estimated parameters were performed. Simulation studies were conducted to show the accurate and fast convergence of the proposed finite-time (FT) identification algorithm based on a 7-DOF arm of Baxter robot.
High-Order Control Barrier Functions Xiao, Wei; Belta, Calin
IEEE transactions on automatic control,
07/2022, Letnik:
67, Številka:
7
Journal Article
Recenzirano
We approach the problem of stabilizing a dynamical system while optimizing a cost and satisfying safety constraints and control limitations. For (nonlinear) affine control systems and quadratic ...costs, it has been shown that control barrier functions (CBFs) guaranteeing safety and control Lyapunov functions (CLFs) enforcing convergence can be used to (conservatively) reduce the optimal control problem to a sequence of quadratic programs (QPs). Existing works in this category have two main limitations. First, with one exception, they are based on the assumption that the relative degree of the system with respect to a function enforcing a safety constraint is one. Second, the QPs can easily become infeasible, in particular for problems with many safety constraints and tight control limitations. We propose high-order CBFs (HOCBFs), which can accommodate systems of arbitrary relative degrees. For each safety constraint, by using Lyapunov-like conditions, we construct a set of controls that renders the intersection of a set of sets forward invariant, which implies the satisfaction of the original constraint. We formulate optimal control problems with constraints given by HOCBF and CLF, and propose two methods-the penalty method and the parameterization method-to address the feasibility problem. Finally, we show how our methodology can be extended for safe navigation in unknown environments with long-term feasibility. We illustrate the proposed framework on adaptive cruise control and robot control problems.
A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While ...recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design choices due to the underlying variation in setups. In this letter, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. We further identify architectural and algorithmic techniques that improve performance, such as a hierarchical decomposition of the robot control learning, a multimodal transformer encoder, discrete latent plans and a self-supervised contrastive loss that aligns video and language representations. By combining the results of our investigation with our improved model components, we are able to present a novel approach that significantly outperforms the state of the art on the challenging language conditioned long-horizon robot manipulation CALVIN benchmark. We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language.
Nowadays, the most adopted model for the design and control of soft robots is the piecewise constant curvature model, with its consolidated benefits and drawbacks. In this work, an alternative model ...for multisection soft manipulator dynamics is presented based on a discrete Cosserat approach, in which the continuous Cosserat model is discretized by assuming a piecewise constant strain along the soft arm. As a consequence, the soft manipulator state is described by a finite set of constant strains. This approach has several advantages with respect to the existing models. First, it takes into account shear and torsional deformations, which are both essential to cope with out-of-plane external loads. Furthermore, it inherits desirable geometrical and mechanical properties of the continuous Cosserat model, such as intrinsic parameterization and greater generality. Finally, this approach allows to extend to soft manipulators, the recursive composite-rigid-body and articulated-body algorithms, whose performances are compared through a cantilever beam simulation. The soundness of the model is demonstrated through extensive simulation and experimental results.
Control systems for powered prosthetic legs typically divide the gait cycle into several periods with distinct controllers, resulting in dozens of control parameters that must be tuned across users ...and activities. To address this challenge, this paper presents a control approach that unifies the gait cycle of a powered knee- ankle prosthesis using a continuous, user-synchronized sense of phase. Virtual constraints characterize the desired periodic joint trajectories as functions of a phase variable across the entire stride. The phase variable is computed from residual thigh motion, giving the amputee control over the timing of the prosthetic joint patterns. This continuous sense of phase enabled three transfemoral amputee subjects to walk at speeds from 0.67 to 1.21 m/s and slopes from -2.5° to +9.0°. Virtual constraints based on task-specific kinematics facilitated normative adjustments in joint work across walking speeds. A fixed set of control gains generalized across these activities and users, which minimized the configuration time of the prosthesis.
Terrain geometry is, in general, nonsmooth, nonlinear, nonconvex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This article presents the complete ...control pipeline capable of handling the aforementioned problems in real-time. We formulate a trajectory optimization problem that jointly optimizes over the base pose and footholds, subject to a height map. To avoid converging into undesirable local optima, we deploy a graduated optimization technique. We embed a compact, contact-force free stability criterion that is compatible with the nonflat ground formulation. Direct collocation is used as transcription method, resulting in a nonlinear optimization problem that can be solved online in less than ten milliseconds. To increase robustness in the presence of external disturbances, we close the tracking loop with a momentum observer. Our experiments demonstrate stair climbing, walking on stepping stones, and over gaps, utilizing various dynamic gaits.
In the feature/object tracking of eye-in-hand visual servoing, 2D motion estimation relying only on image plane feedback is easily affected by vision occlusion, blurring, or poor lighting. For the ...commonly-used template matching method, tracking performance greatly depends on the image quality. Fiber Bragg gratings (FBGs), a type of high-frequency flexible strain sensor, can be used as an assistant device for soft robot control. We propose a method to enhance motion estimation in soft robotic visual servoing by fusing the results from template matching and FBG wavelength shifts to achieve more accurate tracking in applications such as minimally invasive surgery. Path following performance is validated in a simulated laparoscopic scene and LEGO-constructed scene, demonstrating significant improvement to feature tracking and robot motion, even under external forces.
A challenging problem in robotics is how to control multiple robots cooperatively and safely in real-world applications. Yet, developing multi-robot control methods from the perspective of safe ...multi-agent reinforcement learning (MARL) has merely been studied. To fill this gap, in this study, we investigate safe MARL for multi-robot control on cooperative tasks, in which each individual robot has to not only meet its own safety constraints while maximising their reward, but also consider those of others to guarantee safe team behaviours. Firstly, we formulate the safe MARL problem as a constrained Markov game and employ policy optimisation to solve it theoretically. The proposed algorithm guarantees monotonic improvement in reward and satisfaction of safety constraints at every iteration. Secondly, as approximations to the theoretical solution, we propose two safe multi-agent policy gradient methods: Multi-Agent Constrained Policy Optimisation (MACPO) and MAPPO-Lagrangian. Thirdly, we develop the first three safe MARL benchmarks—Safe Multi-Agent MuJoCo (Safe MAMuJoCo), Safe Multi-Agent Robosuite (Safe MARobosuite) and Safe Multi-Agent Isaac Gym (Safe MAIG) to expand the toolkit of MARL and robot control research communities. Finally, experimental results on the three safe MARL benchmarks indicate that our methods can achieve state-of-the-art performance in the balance between improving reward and satisfying safety constraints compared with strong baselines. Demos and code are available at the link (https://sites.google.com/view/aij-safe-marl/).2
•The problem of safe multi-agent reinforcement learning is formulated.•Multi-agent constrained policy optimisation (MACPO) method is proposed.•MACPO ensures both safety constraints satisfaction and monotonic performance improvement guarantee.•Three safe MARL benchmarks are developed: Safe Multi-Agent MuJoCo (Safe MAMuJoCo), Safe Multi-Agent Robosuite (Safe MARobosuite) and Safe Multi-Agent Isaac Gym (Safe MAIG).•Experiments on multiple benchmark environments confirm the effectiveness of MACPO and MAPPO-Lagrangian.
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