Learning Stable In-Grasp Manipulation in a Non-Dropping Action Space

Traditionally, dexterous manipulation controllers are designed using analytic models constrained by strong assumptions about the hand and the objects being manipulated. Reinforcement learning (RL) has become another common approach in which skills are explored openly in an end-to-end manner but is inefficient because of unnoticeable instability and conflicts in learning objectives. This paper attempts to efficiently explore stable and accurate manipulation skills by decomposing dexterous skills into multiple simpler/analyzable components. Each skill component is subsequently learned with constraints and guidance from classical physics and control theory. Our work shows that for stable grasp, in-grasp reposition/reorientation with different objects, sensor/motor noise, latency, and frictional conditions, skill learning becomes efficient and stable with prior knowledge from theory.

Integrated Grasping Controller Leveraging Optical Proximity Sensors for Simultaneous Contact, Impact Reduction, and Force Control

Grasping an unknown object is difficult for robot hands. When the characteristics of the object are unknown, knowing how to plan the speed at and width to which the fingers are narrowed is difficult. In this paper, we propose a method to realize the three functions of simultaneous finger contact, impact reduction, and contact force control, which enable effective grasping of an unknown object. We accomplish this by using a control framework called multiple impedance control, which was proposed in a previous study. The advantage of this control is that multiple functions can be realized without switching control laws. The previous study achieved two functions, impact reduction and contact force control, with a two layers of impedance control which was applied independently to individual fingers. In this paper, a new idea of virtual dynamics that treats multiple fingers comprehensively is introduced, which enables the function of simultaneous contact without compromising the other two functions. This research provides a method to achieve delicate grasping by using proximity sensors. For the effectiveness of the proposed method, please refer to https://youtu.be/q0OrJBal4yA.

Stochastic Approach for Modeling a Soft Robotic Finger with Creep Behavior

Soft robots have high adaptability and safeness which are derived from their softness, and therefore it is paid attention to use them in human society. However, the controllability of soft robots is not enough to perform dexterous behaviors when considering soft robots as alternative laborers for humans. The model-based control is effective to achieve dexterous behaviors. When considering building a model which is suitable for control, there are problems based on their special properties such as the creep behavior or the variability of motion. In this paper, the lumped parameterized model with viscoelastic joints for a soft finger is established for the creep behavior. Parameters are expressed as distributions, which makes it possible to take into account the variability of motion. Furthermore, stochastic analyses are performed based on the parameters' distribution. They show high adaptivity compared with experimental results and also enable the investigation of the effects of parameters for robots' variability.

Smoothly Connected Preemptive Impact Reduction and Contact Impedance Control

This study proposes novel control methods that lower impact force by preemptive movement and smoothly transition to conventional contact impedance control. These suggested techniques are for force control-based robots and position/velocity control-based robots, respectively. Strong impact forces have a negative influence on multiple robotic tasks. Recently, preemptive impact reduction techniques that expand conventional contact impedance control by using proximity sensors have been examined. However, a seamless transition from impact reduction to contact impedance control has not yet been accomplished. The proposed methods utilize a serial combined impedance control framework to solve this problem. The preemptive impact reduction feature can be added to the already implemented impedance controller because the parameter design is divided into impact reduction and contact impedance control. There is no undesirable contact force during the transition. Furthermore, even though the preemptive impact reduction employs a crude optical proximity sensor, the influence of reflectance is minimized using a virtual viscous force. Analyses and real-world experiments confirm these benefits.