Bio-Inspired Grasping Controller for Sensorized 2-DoF Grippers

We present a holistic grasping controller, combining free-space position control and in-contact force-control for reliable grasping given uncertain object pose estimates. Employing tactile fingertip sensors, undesired object displacement during grasping is minimized by pausing the finger closing motion for individual joints on first contact until force-closure is established. While holding an object, the controller is compliant with external forces to avoid high internal object forces and prevent object damage. Gravity as an external force is explicitly considered and compensated for, thus preventing gravity-induced object drift. We evaluate the controller in two experiments on the TIAGo robot and its parallel-jaw gripper proving the effectiveness of the approach for robust grasping and minimizing object displacement. In a series of ablation studies, we demonstrate the utility of the individual controller components

Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot

The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks. An important line of research in this regard is that of grasp force control, which aims to manipulate objects safely by limiting the amount of force exerted on the object. While prior works have either hand-modeled their force controllers, employed model-based approaches, or have not shown sim-to-real transfer, we propose a model-free deep reinforcement learning approach trained in simulation and then transferred to the robot without further fine-tuning. We therefore present a simulation environment that produces realistic normal forces, which we use to train continuous force control policies. An evaluation in which we compare against a baseline and perform an ablation study shows that our approach outperforms the hand-modeled baseline and that our proposed inductive bias and domain randomization facilitate sim-to-real transfer. Code, models, and supplementary videos are available on https://sites.google.com/view/rl-force-ctr

Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing

This work deals with a practical everyday problem: stable object placement on flat surfaces starting from unknown initial poses. Common object-placing approaches require either complete scene specifications or extrinsic sensor measurements, e.g., cameras, that occasionally suffer from occlusions. We propose a novel approach for stable object placing that combines tactile feedback and proprioceptive sensing. We devise a neural architecture that estimates a rotation matrix, resulting in a corrective gripper movement that aligns the object with the placing surface for the subsequent object manipulation. We compare models with different sensing modalities, such as force-torque and an external motion capture system, in real-world object placing tasks with different objects. The experimental evaluation of our placing policies with a set of unseen everyday objects reveals significant generalization of our proposed pipeline, suggesting that tactile sensing plays a vital role in the intrinsic understanding of robotic dexterous object manipulation. Code, models, and supplementary videos are available at https://sites.google.com/view/placing-by-touching

ToBI - Team of Bielefeld A Human-Robot Interaction System for RoboCup@Home 2017

Wachsmuth S, Lier F, Meyer zu Borgsen S, Kummert J, Lach L, Sixt D. ToBI - Team of Bielefeld A Human-Robot Interaction System for RoboCup@Home 2017. Presented at the RoboCup 2017, Nagoya.The Team of Bielefeld (ToBI) has been founded in 2009. The RoboCup teams’ activities are embedded in a long-term research agenda towards human-robot interaction with laypersons in regular and smart home environments. The RoboCup@Home competition is an im- portant benchmark and milestone for this goal in terms of robot capabilities as well as the system integration effort. In order to achieve a robust and stable system performance, we apply a systematic approach for reproducible robotic experimentation including automatic tests. For RoboCup 2017, we plan to enhance this approach by simulating complete RoboCup@Home tasks. We further extend it to the RoboCup@Home standard platform Pepper. Similar to the Nao platform, the Pepper comes with its own runtime and development eco-system. Thus, one of the chal- lenges will be the cross-platform transfer of capabilities between robots based on different eco-system, e.g. the utilized middleware and application layers. In this paper, we will present a generic approach to such issues: the Cognitive Interaction Toolkit. The overall framework inherently supports the idea of open research and offers direct access to reusable components and reproducible systems via a web-based catalog. A main focus of research at Bielefeld are robots as an ambient host in a smart home or for instance as a museum’s guide. Both scenarios are highly relevant for the RoboCup@Home standard platform competition. Skills developed in these domains will be transferred to the RoboCup@Home scenarios

Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa Ependymoma

Despite the histological similarity of ependymomas from throughout the neuroaxis, the disease likely comprises multiple independent entities, each with a distinct molecular pathogenesis. Transcriptional profiling of two large independent cohorts of ependymoma reveals the existence of two demographically, transcriptionally, genetically, and clinically distinct groups of posterior fossa (PF) ependymomas. Group A patients are younger, have laterally located tumors with a balanced genome, and are much more likely to exhibit recurrence, metastasis at recurrence, and death compared with Group B patients. Identification and optimization of immunohistochemical (IHC) markers for PF ependymoma subgroups allowed validation of our findings on a third independent cohort, using a human ependymoma tissue microarray, and provides a tool for prospective prognostication and stratification of PF ependymoma patients

Cytogenetic Prognostication Within Medulloblastoma Subgroups

PURPOSE: Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. PATIENTS AND METHODS: Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. RESULTS: Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. CONCLUSION: Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials

TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots

Lach LM, Ferro F, Haschke R. TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots. In: RoboTac workshop: Visuo-Tactile Perception, Learning, Control for Manipulation and HRI. 2021

Learning safe placement of objects based on tactile feedback

Härtel M, Leins D, Lach LM, Haschke R, eds. Learning safe placement of objects based on tactile feedback. RoboTac workshop: Visuo-Tactile Perception, Learning, Control for Manipulation and HRI. 2023

Tactile Sensing for Stable Object Placing

Lach LM, Funk N, Haschke R, Ritter H, Peters J, Chalvatzaki G. Tactile Sensing for Stable Object Placing. In: TouchProcessing workshop @ NeurIPS. 2023

Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing

Lach LM, Funk N, Haschke R, Ritter H, Peters J, Chalvatzaki G. Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing. In: RoboTac workshop @ IROS. 2023