Finger-gaiting manipulation is an important skill to achieve large-angle in-hand re-orientation of objects. However, achieving these gaits with arbitrary orientations of the hand is challenging due to the unstable nature of the task. In this work, we use model-free reinforcement learning (RL) to learn finger-gaiting only via precision grasps and demonstrate finger-gaiting for rotation about an axis using only on-board proprioceptive and tactile feedback. To tackle the inherent instability of precision grasping, we propose the use of initial state distributions that enable effective exploration of the state space. Our method can learn finger gaiting with better sample complexity than the state-of-the-art. The policies we obtain are robust to noise and perturbations, and transfer to novel objects. Videos can be found at https://roamlab.github.io/learnfg/

Teleoperation is a valuable tool for robotic manipulators in highly unstructured environments. However, finding an intuitive mapping between a human hand and a nonanthropomorphic robot hand can be difficult, due to the hands’ dissimilar kinematics. In this article, we seek to create a mapping between the human hand and a fully actuated, nonanthropomorphic robot hand that is intuitive enough to enable effective real-time teleoperation, even for novice users. To accomplish this, we propose a low-dimensional teleoperation subspace that can be used as an intermediary for mapping between hand pose spaces. We present two different methods to define the teleoperation subspace: an empirical definition, which requires a person to define hand motions in an intuitive, hand-specific way, and an algorithmic definition, which is kinematically independent and uses objects to define the subspace. We use each of these definitions to create a teleoperation mapping for different hands. One of the main contributions of this article is the validation of both the empirical and algorithmic mappings with teleoperation experiments controlled by ten novices and performed on two kinematically distinct hands. The experiments show that the proposed subspace is relevant to teleoperation, intuitive enough to enable control by novices, and can generalize to nonanthropomorphic hands with different kinematics.Note to Practitioners—As robots move into our warehouses, workplaces, and homes, it is important to develop robotic controls that are intuitive and easy for novices to use. In particular, teleoperation can be valuable to guide robots when they encounter situations that autonomous programs are not prepared to deal with. In this article, we focus specifically on robotic grasping using nonanthropomorphic hands. Our method is intended for novice users to intuitively teleoperate such robots. We show that the teleoperation subspace we use can effectively enable pick-and-place tasks and in-hand manipulation tasks and that it is intuitive for novice operators. Our subspace outperforms state-of-the-art methods for pick-and-place tasks and performs as well as state-of-the-art methods for in-hand manipulation.

In this paper, we propose a method for generating undulatory gaits for snake robots. Instead of starting from a pre-defined movement pattern such as a serpenoid curve, we use a Model Predictive Control (MPC) approach to automatically generate effective locomotion gaits via trajectory optimization. An important advantage of this approach is that the resulting gaits are automatically adapted to the environment that is being modeled as part of the snake dynamics. To illustrate this, we use a novel model for anisotropic dry friction, along with existing models for viscous friction and fluid dynamic effects such as drag and added mass. For each of these models, gaits generated without any change in the method or its parameters are as efficient as Pareto-optimal serpenoid gaits tuned individually for each environment. Furthermore, the proposed method can also produce more complex or irregular gaits, e.g. for obstacle avoidance or executing sharp turns.

Grasp synergies represent a useful idea to reduce grasping complexity without compromising versatility. Synergies describe coordination patterns between joints, either in terms of position (joint angles) or effort (joint torques). In both of these cases, a grasp synergy can be represented as a low-dimensional manifold lying in the high-dimensional joint posture or torque space. In this article, we use the term mechanically realizable manifolds to refer to the subset of such manifolds (in either posture or torque space) that can be achieved via mechanical coupling of the joints in underactuated hands. We present a method to optimize the design parameters of an underactuated hand in order to shape the mechanically realizable manifolds to fit a predefined set of desired grasps. Our method guarantees that the resulting synergies can be physically implemented in an underactuated hand, and will enable the resulting hand to both reach the desired grasp postures and achieve quasi-static equilibrium while loading the grasps. We demonstrate this method on three concrete design examples motivated by a real use case, and evaluate and compare their performance in practice.

Passive reaction effects in grasp stability analysis occur when the contact forces and joint torques applied by a grasp change in response to external disturbances applied to the grasped object. For example, nonbackdrivable actuators (e.g., highly geared servos) will passively resist external disturbances without an actively applied command; for numerous robot hands using such motors, these effects can be highly beneficial as they increase grasp resistance without requiring active control. In this article, we introduce a grasp stability analysis method that can model these effects, and, for a given grasp, distinguish between disturbances that will be passively resisted and those that will not. We find that, in order to achieve this, the grasp model must include accurate energetic constraints. One way to achieve this is to consider the maximum dissipation principle (MDP), a part of the Coulomb friction model that is rarely used in grasp stability analysis. However, the MDP constraints are nonconvex, and difficult to solve efficiently. We thus introduce a convex relaxation method, along with an algorithm that successively refines this relaxation locally in order to obtain solutions to arbitrary accuracy efficiently. Our resulting algorithm can determine if a grasp is passively stable, solve for equilibrium contact forces and compute optimal actuator commands for stability. Its implementation is publicly available as part of the open-source GraspIt! simulator.

Hand synergies, or joint coordination patterns, have become an effective tool for achieving versatile robotic grasping with simple hands or planning algorithms. Here we propose a method to determine the hand synergies such that they can be physically implemented in an underactuated fashion. Given a kinematic hand model and a set of desired grasps, our algorithm optimizes a Mechanically Realizable Manifold designed to be achievable by a physical underactuation mechanism, enabling the resulting hand to achieve the desired grasps with few actuators. Furthermore, in contrast to existing methods for determining synergies which are only concerned with hand posture, our method explicitly optimizes the stability of the target grasps. We implement this method in the design of a three-finger single-actuator hand as an example, and evaluate its effectiveness numerically and experimentally.

In this paper, we focus on the following problem in multifingered robotic grasping: assuming that an external wrench is being applied to a grasped object, will the contact forces between the hand and the object, as well as the hand joints, respond in such a way to preserve quasi-static equilibrium? In particular, we assume that there is no change in the joint torques being actively exerted by the motors; any change in contact forces and joint torques is due exclusively to passive effects arising in response to the external disturbance. Such passive effects include, for example, joints that are driven by highly geared motors (a common occurrence in practice) and thus do not back drive in response to external torques. To account for nonlinear phenomena encountered in such cases, and which existing methods do not consider, we formulate the problem as a mixed-integer program used in the inner loop of an iterative solver. We present evidence showing that this formulation captures important effects for assessing the stability of a grasp employing some of the most commonly used actuation mechanisms.