A model-free approach to fingertip slip and disturbance detection for grasp stability inference

Robotic capacities in object manipulation are incomparable to those of humans. Besides years of learning, humans rely heavily on the richness of information from physical interaction with the environment. In particular, tactile sensing is crucial in providing such rich feedback. Despite its potential contributions to robotic manipulation, tactile sensing is less exploited; mainly due to the complexity of the time series provided by tactile sensors. In this work, we propose a method for assessing grasp stability using tactile sensing. More specifically, we propose a methodology to extract task-relevant features and design efficient classifiers to detect object slippage with respect to individual fingertips. We compare two classification models: support vector machine and logistic regression. We use highly sensitive Uskin tactile sensors mounted on an Allegro hand to test and validate our method. Our results demonstrate that the proposed method is effective in slippage detection in an online fashion.Comment: IEEE International Conference on Development and Learning 2023 (ICDL), Nov 2023, Macau, Chin

A Dynamical System Approach to Task-Adaptation in Physical Human-Robot Interaction

The goal of this work is to enable robots to intelligently and compliantly adapt their motions to the intention of a human during physical Human-Robot Interaction (pHRI) in a multi-task setting. We employ a class of parameterized dynamical systems that allows for smooth and adaptive transitions between encoded tasks. To comply with human intention, we propose a mechanism that adapts generated motions (i.e., the desired velocity) to those intended by the human user (i.e., the real velocity) thereby switching to the most similar task. We provide a rigorous analytical evaluation of our method in terms of stability, convergence, and optimality yielding an interaction behavior which is safe and intuitive for the human. We investigate our method through experimental evaluations ranging in different setups: a 3-DoF haptic device, a 7-DoF manipulator and a mobile platform

Cognitive mechanism in synchronized motion: An internal predictive model for manual tracking control

Many daily tasks involve spatio-temporal coordination between two agents. Study of such coordinated actions in human-human and human-robot interaction has received increased attention of late. In this work, we use the mirror paradigm to study coupling of hand motion in a leader-follower game. The main aim of this study is to model the motion of the follower, given a particular motion of the leader. We propose a mathematical model consistent with the internal model hypothesis and the delays in the sensorimotor system. A qualitative comparison of data collected in four human dyads shows that it is possible to successfully model the motion of the follower

Angular Motion Control Using a Closed-Loop CPG for a Water-Running Robot

The Basilisk Lizard’s striking ability to sustain highly dynamic legged locomotion on a range of surfaces from hard-ground to water is a remarkable feat [1]. Most legged robots would have difficulty emulating this animal’s ability to robustly locomote on yielding or deforming surfaces. Therefore, to explore the dynamics of legged locomotion in this regime, we are studying the design of a bio-inspired water-running robot. Analyzing water-running dynamics may also help us gain insight into mobility on other yielding surfaces, such as granular media and mud

From Human Physical Interaction To Online Motion Adaptation Using Parameterized Dynamical Systems

In this work, we present an adaptive motion planning approach for impedance-controlled robots to modify their tasks based on human physical interactions. We use a class of parameterized time-independent dynamical systems for motion generation where the modulation of such parameters allows for motion flexibility. To adapt to human interactions, we update the parameter of our dynamical system in order to reduce the tracking error (i.e., between the desired trajectory generated by the dynamical system and the real trajectory influenced by the human interaction). We provide analytical analysis and several simulations of our method. Finally, we investigate our approach through real world experiments with 7-DOF KUKA LWR 4+ robot performing tasks such as polishing and pick-and-place

Robust Walking Using Peicewise Linear Spring

Having a direct impact on the energy efficiency has made the compliance a favorable element in the robotic systems. Moreover, legged system can benefit from compliance for stability, speed, adaptability and robustness. Recently, we have studied the effects of compliant spine in quadrupedal robots. We have observed that having nonlinearity in the spine compliance can set a better trade-off between speed and energy efficiency. Similar to the spine in quadruped robots, compliance at the hip joint of bipedal robots can also improve the walking performance such as robustness. Here, we test the efficacy of piecewise linear hip compliance for robust bipedal walking

Energy Efficient Locomotion with Adaptive Natural Oscillator

For robotic systems, energy efficiency is one the most crucial goals. Many studies have been done to accomplish this goal from design and control point of view. In the second view, one of the preferred method is to design the desired trajectory in harmony with the dynamics of the system; i.e. natural dynamics exploitation. Assuming a structure for the desired trajectory, such as sinusoidal trajectories, we can have a parametrized control system as in CPG-Network. Therefore, having an adaptation method for those parameters to reach energy efficiency can be beneficial to control of robotic systems