We propose a framework for optimizing a planar parallel-jaw gripper for use with multiple objects. While optimizing general-purpose grippers and contact locations for grasps are both well studied, co-optimizing grasps and the gripper geometry to execute them receives less attention. As such, our framework synthesizes grippers optimized to stably grasp sets of polygonal objects. Given a fixed number of contacts and their assignments to object faces and gripper jaws, our framework optimizes contact locations along these faces, gripper pose for each grasp, and gripper shape. Our key insights are to pose shape and contact constraints in frames fixed to the gripper jaws, and to leverage the linearity of constraints in our grasp stability and gripper shape models via an augmented Lagrangian formulation. Together, these enable a tractable nonlinear program implementation. We apply our method to several examples. The first illustrative problem shows the discovery of a geometrically simple solution where possible. In another, space is constrained, forcing multiple objects to be contacted by the same features as each other. Finally a toolset-grasping example shows that our framework applies to complex, real-world objects. We provide a physical experiment of the toolset grasps.

In this work, we build on our method for manipulating unknown objects via contact configuration regulation: the estimation and control of the location, geometry, and mode of all contacts between the robot, object, and environment. We further develop our estimator and controller to enable manipulation through more complex contact interactions, including intermittent contact between the robot/object, and multiple contacts between the object/environment. In addition, we support a larger set of contact geometries at each interface. This is accomplished through a factor graph based estimation framework that reasons about the complementary kinematic and wrench constraints of contact to predict the current contact configuration. We are aided by the incorporation of a limited amount of visual feedback; which when combined with the available F/T sensing and robot proprioception, allows us to differentiate contact modes that were previously indistinguishable. We implement this revamped framework on our manipulation platform, and demonstrate that it allows the robot to perform a wider set of manipulation tasks. This includes, using a wall as a support to re-orient an object, or regulating the contact geometry between the object and the ground. Finally, we conduct ablation studies to understand the contributions from visual and tactile feedback in our manipulation framework. Our code can be found at: https://github.com/mcubelab/pbal.

We present an approach to robotic manipulation of unknown objects through regulation of the object's contact configuration: the location, geometry, and mode of all contacts between the object, robot, and environment. A contact configu-ration constrains the forces and motions that can be applied to the object; however, synthesizing these constraints generally requires knowledge of the object's pose and geometry. We develop an object-agnostic approach for estimation and control that circumvents this need. Our framework directly estimates a set of wrench and motion constraints which it uses to regulate the contact configuration. We use this to reactively manipulate unknown planar objects in the gravity plane. A video describing our work can be found on our project page: http://mcube.mit.edu/research/contactConfig.html.

We propose a framework for co-optimizing the shape and motion of rigid robotic effectors for planar tasks. While planning object and robot-object contact trajectories is extensively studied, designing an effector that can execute the planned trajectories receives less attention. As such, our framework synthesizes an object trajectory and object-effector contact trajectory into an effector trajectory and shape that (a) does not penetrate the object, (b) makes contact with the object as specified, and (c) optimizes a user-specified objective. This simplifies manipulator control by encoding task-specific contact information in the effector's geometry. Our key insight is posing these requirements as constraints in the effector's reference frame, preventing the need for explicit parameterization of the effector shape. This prevents artificial restrictions on the shape design space. Importantly, it also facilitates posing the shape and motion design problem as a tractable nonlinear program. Our method is particularly useful for problems where the shape of the effector surface must be precisely chosen to achieve a task. We apply our method to several such problems, including jar-opening and picking up objects in constrained spaces. We evaluate the performance and computational cost of our method, and provide a physical experiment of a robotic arm picking up a screwdriver from a table with a designed tool.

A majority of microrobots are constructed using compliant materials that are difficult to model analytically, limiting the utility of traditional model-based controllers. Challenges in data collection on microrobots and large errors between simulated models and real robots make current model-based learning and sim-to-real transfer methods difficult to apply. We propose a novel framework residual model learning (RML) that leverages approximate models to substantially reduce the sample complexity associated with learning an accurate robot model. We show that using RML, we can learn a model of the Harvard Ambulatory MicroRobot (HAMR) using just 12 seconds of passively collected interaction data. The learned model is accurate enough to be leveraged as "proxy-simulator" for learning walking and turning behaviors using model-free reinforcement learning algorithms. RML provides a general framework for learning from extremely small amounts of interaction data, and our experiments with HAMR clearly demonstrate that RML substantially outperforms existing techniques.

We present a hybrid differential dynamic programming (DDP) algorithm for closed-loop execution of manipulation primitives with frictional contact switches. Planning and control of these primitives is challenging as they are hybrid, under-actuated, and stochastic. We address this by developing hybrid DDP both to plan finite horizon trajectories with a few contact switches and to create linear stabilizing controllers. We evaluate the performance and computational cost of our framework in ablations studies for two primitives: planar pushing and planar pivoting. We find that generating pose-to-pose closed-loop trajectories from most configurations requires only a couple (one to two) hybrid switches and can be done in reasonable time (one to five seconds). We further demonstrate that our controller stabilizes these hybrid trajectories on a real pushing system. A video describing our work can be found at https://youtu.be/YGSe4cUfq6Q.

We present the design of an active two-phase finger for mechanically mediated dexterous manipulation. The finger enables re-orientation of a grasped object by using a pneumatic braking mechanism to transition between free-rotating and fixed (i.e., braked) phases. Our design allows controlled high-bandwidth (5 Hz) phase transitions independent of the grasping force for manipulation of a variety of objects. Moreover, its thin profile (1 cm) facilitates picking and placing in clutter. Finally, the design features a sensor for measuring fingertip rotation to support feedback control. We experimentally characterize the finger's load handling capacity in the brake phase and rotational resistance in the free phase. We also demonstrate several pick-and-place manipulations common to industrial and laboratory automation settings that are simplified by our design.

We present an off-board phase estimator and controller for leg position near the resonance of the Harvard Ambulatory MicroRobot's (HAMR) two degree-of-freedom transmission. This control system is a first step towards leveraging the significant increase in stride length at transmission resonance for faster and more efficient locomotion. We experimentally characterize HAMR's transmission and determine that actuator phase is a sufficient proxy for leg phase across the range of useful operating frequencies (1-120Hz). An estimator is developed to determine actuator phase using off-board position sensors and it converges within a cycle on average. We also fit a nonlinear dynamic model of the transmission to the experimental data, and utilize the model to determine a suitable open-loop resonant leg trajectory and define feed forward control inputs. This resonant (100Hz) trajectory is theoretically 50% more efficient than pre-resonant high speed running trajectories. The controller converges to this trajectory in 0.05 ± 0.02 seconds (5.3 ± 2.4 cycles) in air, and in 0.05 ± 0.01 seconds (4.7 ± 0.6 cycles) under perturbations that approximate ground contact.

This paper presents an off-board trajectory controller for a range of stride frequencies (2-45 Hz) that enables zero-radius turns and holonomic control on one of the smallest and fastest legged robots, the Harvard Ambulatory MicroRobot (HAMR). An experimental model is used as the basis for control to capture the highly nonlinear response of the robot to input signals. Closed-loop trajectories are performed with an RMS position error at or below 0.3 body lengths (BL) using gaits at speeds up to 6.5 BL/s (29.4 cm/s) for straight-line and sinusoidal trajectories.

This paper develops a custom motion capture system that uses vision-based methods to rapidly and accurately track the body and leg position/orientation of a 1.43g legged microrobot, the Harvard Ambulatory MicroRobot (HAMR). Two new generalized metrics for quantifying locomotion performance are defined: amplitude-normalized stride correlation, and percent ineffective stance. Six different gaits are run on HAMR to validate the experimental setup and establish baseline performance. Furthermore, HAMR is compared with the cockroach, Blaberus Discoidalis, and with other legged robots. Future studies can leverage the experimental setup to study gait selection and transitions for small legged systems.

Full autonomy remains a challenge for miniature robotic platforms due to mass and size requirements of on-board power and control electronics. This paper presents a solution to these challenges with a 2.3g autonomous legged robot. An off-the-shelf optical mouse sensor is adapted for use on the Harvard Ambulatory Microrobot (HAMR) by reducing the sensor weight by 36% and achieving a position error below 11% when suspended 3mm above a cardstock surface. The position data is combined with data from a gyroscope for feedback control of both position and orientation. A microcontroller processes the sensor data and commands a controlled gait to HAMR that is powered by a battery, a boost converter and high voltage drive electronics. Solar cells are used as an alternative source providing enough power for autonomous operation of the robot. The resulting deviation for a controlled straight-line walk using both sensors to minimize lateral deviation and angular error is only 4.6%, compared to an error of 31% in an uncontrolled, straight-line walk.

This paper presents a non-linear, dynamic model of the flexure-based transmission in the Harvard Ambulatory Microrobot (HAMR). The model is derived from first principles and has led to a more comprehensive understanding of the components in this transmission. In particular, an empirical model of the dynamic properties of the compliant Kapton flexures is developed and verified against theoretical results from beam and vibration theory. Furthermore, the fabrication of the piezoelectric bending actuators that drive the transmission is improved to match theoretical performance predictions. The transmission model is validated against experimental data taken on HAMR for the quasi-static (1–10 Hz) operating mode, and is used to redesign the transmission for improved performance in this regime. The model based redesign results in a 266% increase in the work done by the foot when compared to a previous version of HAMR. This leads to a payload capacity of 2.9g, which is ∼ 2× the robot's mass and a 114% increase. Finally, the model is validated in the dynamic regime (40–150 Hz) and the merits of a second order linear approximation are discussed.

This paper addresses the self-assembly of a large team of autonomous boats into floating platforms. We describe the design of individual boats, the systems concept, the algorithms, the software architecture and experimental results with prototypes that are 1:12 scale realizations of modified ISO shipping containers, with the goal of demonstrating self-assembly into large maritime structures such as air strips, bridges, harbors or sea bases. Each container is a robotic module capable of holonomic motion that can dock in a brick pattern to form arbitrary shapes. Over 60 modules were built of varying capability. The docking mechanism is designed to be robust to large disturbances that can be expected in the high seas. The docking mechanism also incorporates adjustable stiffness so that the conglomerate can comply to waves representative of sea state three, and have the ability to dynamically stiffen as required. The component modules for autonomous assembly, docking and simultaneous collision-free planning as well as the software architecture are presented along with the description of experimental verification.