Refining 6-DoF Grasps with Context-Specific Classifiers

In this work, we present GraspFlow, a refinement approach for generating context-specific grasps. We formulate the problem of grasp synthesis as a sampling problem: we seek to sample from a context-conditioned probability distribution of successful grasps. However, this target distribution is unknown. As a solution, we devise a discriminator gradient-flow method to evolve grasps obtained from a simpler distribution in a manner that mimics sampling from the desired target distribution. Unlike existing approaches, GraspFlow is modular, allowing grasps that satisfy multiple criteria to be obtained simply by incorporating the relevant discriminators. It is also simple to implement, requiring minimal code given existing auto-differentiation libraries and suitable discriminators. Experiments show that GraspFlow generates stable and executable grasps on a real-world Panda robot for a diverse range of objects. In particular, in 60 trials on 20 different household objects, the first attempted grasp was successful 94% of the time, and 100% grasp success was achieved by the second grasp. Moreover, incorporating a functional discriminator for robot-human handover improved the functional aspect of the grasp by up to 33%.Comment: IROS 2023, Code and Datasets are available at https://github.com/tasbolat1/graspflo

Dynamic-vision-based force measurements using convolutional recurrent neural networks

In this paper, a novel dynamic Vision-Based Measurement method is proposed to measure contact force independent of the object sizes. A neuromorphic camera (Dynamic Vision Sensor) is utilizused to observe intensity changes within the silicone membrane where the object is in contact. Three deep Long Short-Term Memory neural networks combined with convolutional layers are developed and implemented to estimate the contact force from intensity changes over time. Thirty-five experiments are conducted using three objects with different sizes to validate the proposed approach. We demonstrate that the networks with memory gates are robust against variable contact sizes as the networks learn object sizes in the early stage of a grasp. Moreover, spatial and temporal features enable the sensor to estimate the contact force every 10 ms accurately. The results are promising with Mean Squared Error of less than 0.1 N for grasping and holding contact force using leave-one-out cross-validation method

Constrained Orientation Control of a Spherical Parallel Manipulator via Online Convex Optimization

This paper introduces a new framework for the closed-loop orientation control of spherical parallel manipulators (SPMs) based on the online solution of a convex optimization problem. The aim of solving a constrained optimization problem is to define a reference position for the SPM that remains as close as possible to the ideal reference (i.e., the one for which the top mobile platform has the desired orientation), at the same time keeping the SPM within the set of configurations in which collisions between links and singular configurations are avoided (the so-called feasible workspace). The proposed approach relies on a recently introduced method for obtaining unique inverse kinematics for SPMs and a newly proposed method for generating an approximation of the feasible workspace suitable for fast online optimization. The proposed control scheme is experimentally tested on an Agile Wrist SPM prototype, confirming the performance expected from the theoretical formulation

An open-source 7-DOF wireless human arm motion-tracking system for use in robotics research

10.3390/s20113082Sensors (Switzerland)2011308

Extended Tactile Perception: Vibration Sensing through Tools and Grasped Objects

10.1109/iros51168.2021.96366772021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS