On the Interplay between Mechanical and Computational Intelligence in Robot Hands

Researchers have made tremendous advances in robotic grasping in the past decades. On the hardware side, a lot of robot hand designs were proposed, covering a large spectrum of dexterity (from simple parallel grippers to anthropomorphic hands), actuation (from underactuated to fully actuated), and sensing capabilities (from only open/close states to tactile sensing). On the software side, grasping techniques also evolved significantly, from open-loop control, classical feedback control, to learning-based policies. However, most of the studies and applications follow the one-way paradigm that mechanical engineers/researchers design the hardware first and control/learning experts write the code to use the hand. In contrast, we aim to study the interplay between the mechanical and computational aspects in robotic grasping. We believe both sides are important but cannot solve grasping problems on their own, and both sides are highly connected by the laws of physics and should not be developed separately. We use the term "Mechanical Intelligence" to refer to the ability realized by mechanisms to appropriately respond to the external inputs, and we show that incorporating Mechanical Intelligence with Computational Intelligence is beneficial for grasping. The first part of this thesis is to derive hand underactuation mechanisms from grasp data. The mechanical coordination in robot hands, which is one type of Mechanical Intelligence, corresponds to the concept of dimensionality reduction in Machine Learning. However, the resulted low-dimensional manifolds need to be realizable using underactuated mechanisms. In this project, we first collect simulated grasp data without accounting for underactuation, apply a dimensionality reduction technique (we term it "Mechanically Realizable Manifolds") considering both pre-contact postural synergies and post-contact joint torque coordination, and finally build robot hands based on the resulted low-dimensional models. We also demonstrate a real-world application on a free-flying robot for the International Space Station. The second part is about proprioceptive grasping for unknown objects by taking advantage of hand compliance. Mechanical compliance is intrinsically connected to force/torque sensing and control. In this work, we proposed a series-elastic hand providing embodied compliance and proprioception, and an associated grasping policy using a network of proportional-integral controllers. We show that, without any prior model of the object and with only proprioceptive sensing, a robot hand can make stable grasps in a reactive fashion. The last part is about developing the Mechanical and Computational Intelligence jointly --- to co-optimize the mechanisms and control policies using deep Reinforcement Learning (RL). Traditional RL treats robot hardware as immutable and models it as part of the environment. In contrast, we move the robot hardware out of the environment, express its mechanics as auto-differentiable physics and connect it with the computational policy to create a unified policy (we term this method "Hardware as Policy"), which allows RL algorithms to back-propagate gradients w.r.t both hardware and computational parameters and optimize them in the same fashion. We present a mass-spring toy problem to illustrate this idea, and also a real-world design case of an underactuated hand. The three projects we present in this thesis are meaningful examples to demonstrate the interplay between the mechanical and computational aspects of robotic grasping. In the Conclusion part, we summarize some high-level philosophies and suggestions to integrate Mechanical and Computational Intelligence, as well as the high-level challenges that still exist when pushing this area forward

A Compositional Approach to Certifying the Almost Global Asymptotic Stability of Cascade Systems

In this work, we give sufficient conditions for the almost global asymptotic stability of a cascade in which the inner loop and the unforced outer loop are each almost globally asymptotically stable. Our qualitative approach relies on the absence of chain recurrence for non-equilibrium points of the unforced outer loop, the hyperbolicity of equilibria, and the precompactness of forward trajectories. We show that the required structure of the chain recurrent set can be readily verified, and describe two important classes of systems with this property. We also show that the precompactness requirement can be verified by growth rate conditions on the interconnection term coupling the subsystems. Our results stand in contrast to prior works that require either global asymptotic stability of the subsystems (impossible for smooth systems evolving on general manifolds), time scale separation between the subsystems, or strong disturbance robustness properties of the outer loop. The approach has clear applications in stability certification of cascaded controllers for systems evolving on manifolds.Comment: This version corrects a minor technical error in Definition

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Efficient Geometric Linearization of Moving-Base Rigid Robot Dynamics

The linearization of the equations of motion of a robotics system about a given state-input trajectory, including a controlled equilibrium state, is a valuable tool for model-based planning, closed-loop control, gain tuning, and state estimation. Contrary to the case of fixed based manipulators with prismatic or rotary joints, the state space of moving-base robotic systems such as humanoids, quadruped robots, or aerial manipulators cannot be globally parametrized by a finite number of independent coordinates. This impossibility is a direct consequence of the fact that the state of these systems includes the system's global orientation, formally described as an element of the special orthogonal group SO(3). As a consequence, obtaining the linearization of the equations of motion for these systems is typically resolved, from a practical perspective, by locally parameterizing the system's attitude by means of, e.g., Euler or Cardan angles. This has the drawback, however, of introducing artificial parameterization singularities and extra derivative computations. In this contribution, we show that it is actually possible to define a notion of linearization that does not require the use of a local parameterization for the system's orientation, obtaining a mathematically elegant, recursive, and singularity-free linearization for moving-based robot systems. Recursiveness, in particular, is obtained by proposing a nontrivial modification of existing recursive algorithms to allow for computations of the geometric derivatives of the inverse dynamics and the inverse of the mass matrix of the robotic system. The correctness of the proposed algorithm is validated by means of a numerical comparison with the result obtained via geometric finite difference

Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation

Non-linear model predictive control (nMPC) is a powerful approach to control complex robots (such as humanoids, quadrupeds, or unmanned aerial manipulators (UAMs)) as it brings important advantages over other existing techniques. The full-body dynamics, along with the prediction capability of the optimal control problem (OCP) solved at the core of the controller, allows to actuate the robot in line with its dynamics. This fact enhances the robot capabilities and allows, e.g., to perform intricate maneuvers at high dynamics while optimizing the amount of energy used. Despite the many similarities between humanoids or quadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to UAMs. This paper provides a thorough description of how to use such techniques in the field of aerial manipulation. We give a detailed explanation of the different parts involved in the OCP, from the UAM dynamical model to the residuals in the cost function. We develop and compare three different nMPC controllers: Weighted MPC, Rail MPC, and Carrot MPC, which differ on the structure of their OCPs and on how these are updated at every time step. To validate the proposed framework, we present a wide variety of simulated case studies. First, we evaluate the trajectory generation problem, i.e., optimal control problems solved offline, involving different kinds of motions (e.g., aggressive maneuvers or contact locomotion) for different types of UAMs. Then, we assess the performance of the three nMPC controllers, i.e., closed-loop controllers solved online, through a variety of realistic simulations. For the benefit of the community, we have made available the source code related to this work.Comment: Submitted to Transactions on Robotics. 17 pages, 16 figure

Nonlinear control and synchronization of multiple Lagrangian systems with application to tethered formation flight spacecraft

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 217-228).This dissertation focuses on the synchronization of multiple dynamical systems using contraction theory, with applications to cooperative control of multi-agent systems and synchronization of interconnected dynamics such as tethered formation flight. Inspired by stable combinations of biological systems, contraction nonlinear stability theory provides a systematic method to reduce arbitrarily complex systems into simpler elements. One application of oscillation synchronization is a fully decentralized nonlinear control law, which eliminates the need for any inter-satellite communications. We use contraction theory to prove that a nonlinear control law stabilizing a single-tethered spacecraft can also stabilize arbitrarily large circular arrays of tethered spacecraft, as well as a three-spacecraft inline configuration. The convergence result is global and exponential due to the nature of contraction analysis. The proposed decentralized control strategy is further extended to robust adaptive control in order to account for model uncertainties. Numerical simulations and experimental results validate the exponential stability of the tethered formation arrays by implementing a tracking control law derived from the reduced dynamics.(cont.) This thesis also presents a new synchronization framework that can be directly applied to cooperative control of autonomous aerospace vehicles and oscillation synchronization in robotic manipulation and locomotion. We construct a dynamical network of multiple Lagrangian systems by adding diffusive couplings to otherwise freely moving or flying vehicles. The proposed tracking control law synchronizes an arbitrary number of robots into a common trajectory with global exponential convergence. The proposed control law is much simpler than earlier work in terms of both the computational load and the required signals. Furthermore, in contrast with earlier work which used simple double integrator models, the proposed method permits highly nonlinear systems and is further extended to adaptive synchronization, partial-joint coupling, and concurrent synchronization. Another contribution of the dissertation is a novel nonlinear control approach for underactuated tethered formation flight spacecraft. This is motivated by a controllability analysis that indicates that both array resizing and spin-up are fully controllable by the reaction wheels and the tether motor. This work reports the first propellant-free underactuated control results for tethered formation flight.(cont.) We also fulfill the potential of the proposed strategy by providing a new momentum dumping method. This dissertation work has evolved based on the research philosophy of balancing theoretical work with practicality, aiming at physically intuitive algorithms that can be directly implemented in real systems. In order to validate the effectiveness of the decentralized control and estimation framework, a new suite of hardware has been designed and added to the SPHERES (Synchronize Position Hold Engage and Reorient Experimental Satellite) testbed. Such recent improvements described in this dissertation include a new tether reel mechanism, a force-torque sensor and an air-bearing carriage with a reaction wheel. This thesis also introduces a novel relative attitude estimator, in which a series of Kalman filters incorporate the gyro, force-torque sensor and ultrasound ranging measurements. The closed-loop control experiments can be viewed at ...by Soon-Jo Chung.Sc.D

Design and control of a gravity-assisted underactuated snake robot with application to aircraft wing assembly

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (p. 108-111).We present the design and control of a hyper-articulated robot arm comprising just a few active joints driving a multitude of passive joints. This underactuated arm design was motivated by the need for a compact snake-like robot for assembly operations inside an aircraft wing. The interior of the wing is accessible only through small access portholes distributed along the length. Currently, such assembly operations are performed by human operators who crawl into the wing through its access portholes. The working conditions are ergonomically challenging and result in frequent injuries. The conflicting requirements of small form factor and high payload carrying capacity have been the primary bottlenecks in the development of assembly robots. We propose a nested-channel serial linkage structure for the hyper-articulated arm. When fully contracted, the arm is extremely compact and can access the interior of the wing through its access porthole. Once inside the wing, the arm may be expanded to access distal assembly locations. However, it is impossible to package current actuator technology to meet the payload requirements within the limited size of the robot arm. The joints of the hyper-articulated arm have no dedicated actuators. Instead, they are deployed by modulating gravitational torques. By tilting the base link appropriately, the gravitational torque drives each unactuated link to a desired angular position. With simple, compact locking mechanisms, the arm can change its configuration using the actuated base placed outside the wing. We analyze the system dynamics to gain physical insight into the interaction between the actuated and unactuated degrees of freedom. We make important approximations to capture the dominant effects in the system dynamics so as to facilitate control design.(cont.) The dynamics (actual, as well as approximate) of the unactuated links are essentially 2nd order non-holonomic constraints, for which there are no general control techniques. We present several motion planning algorithms for sequential positioning of the free joints of the robot arm. The motion planning algorithms are formulated as parameterized non-linear two point boundary value problems. These algorithms demonstrate reasonable performance in the absence of disturbances. However, the end-effecter requires accurate positioning to perform assembly operations. To address this issue, we present a sequential closed-loop control algorithm for accurate positioning of the free joints. We synthesize a Lyapunov function to prove the convergence of this control scheme and to generate estimates of the domain of convergence. For faster deployment of the robot arm, multiple free links must move concurrently. We also present several motion planning algorithms to address this problem. We built two prototypes to illustrate the design and actuation concepts. The first prototype has 3 links and has a fixed axis of tilt in the horizontal plane. The second prototype has 4 links and may be tilted about an arbitrary axis in the horizontal plane. The motion planning and closed-loop control algorithms were implemented on both prototypes. The experimental results indicate the efficacy of such control schemes.by Binayak Roy.Ph.D

Synchronization of multiple rigid body systems: a survey

The multi-agent system has been a hot topic in the past few decades owing to its lower cost, higher robustness, and higher flexibility. As a particular multi-agent system, the multiple rigid body system received a growing interest since its wide applications in transportation, aerospace, and ocean exploration. Due to the non-Euclidean configuration space of attitudes and the inherent nonlinearity of the dynamics of rigid body systems, synchronization of multiple rigid body systems is quite challenging. This paper aims to present an overview of the recent progress in synchronization of multiple rigid body systems from the view of two fundamental problems. The first problem focuses on attitude synchronization, while the second one focuses on cooperative motion control in that rotation and translation dynamics are coupled. Finally, a summary and future directions are given in the conclusion