737 research outputs found

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

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    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

    Modularity in Nervous Systems-a Key to Efficient Adaptivity for Deep Reinforcement Learning

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    Schilling M, Hammer B, Ohl FW, Ritter H, Wiskott L. Modularity in Nervous Systems-a Key to Efficient Adaptivity for Deep Reinforcement Learning. Cognitive Computation. 2023.Modularity as observed in biological systems has proven valuable for guiding classical motor theories towards good answers about action selection and execution. New challenges arise when we turn to learning: Trying to scale current computational models, such as deep reinforcement learning (DRL), to action spaces, input dimensions, and time horizons seen in biological systems still faces severe obstacles unless vast amounts of training data are available. This leads to the question: does biological modularity also hold an important key for better answers to obtain efficient adaptivity for deep reinforcement learning? We review biological experimental work on modularity in biological motor control and link this with current examples of (deep) RL approaches. Analyzing outcomes of simulation studies, we show that these approaches benefit from forms of modularization as found in biological systems. We identify three different strands of modularity exhibited in biological control systems. Two of them-modularity in state (i) and in action (ii) spaces-appear as a consequence of local interconnectivity (as in reflexes) and are often modulated by higher levels in a control hierarchy. A third strand arises from chunking of action elements along a (iii) temporal dimension. Usually interacting in an overarching spatio-temporal hierarchy of the overall system, the three strands offer major "factors" decomposing the entire modularity structure. We conclude that modularity with its above strands can provide an effective prior for DRL approaches to speed up learning considerably and making learned controllers more robust and adaptive

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

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    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

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    Lach LM, Funk NW, Haschke R, et al. Placing by Touching: An Empirical Study on the Importance of Tactile Sensing for Precise Object Placing. In: Proc. IROS. 2023.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 on https://sites.google.com/view/placing-by-touching

    Tactile Sensing for Stable Object Placing

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

    Perception and simulation during concept learning

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    Weitnauer E, Goldstone RL, Ritter H. Perception and simulation during concept learning. Psychological Review . 2023.A key component of humans' striking creativity in solving problems is our ability to construct novel descriptions to help us characterize novel concepts. Bongard problems (BPs), which challenge the problem solver to come up with a rule for distinguishing visual scenes that fall into two categories, provide an elegant test of this ability. BPs are challenging for both human and machine category learners because only a handful of example scenes are presented for each category, and they often require the open-ended creation of new descriptions. A new type of BP called physical Bongard problems (PBPs) is introduced, which requires solvers to perceive and predict the physical spatial dynamics implicit in the depicted scenes. The perceiving and testing hypotheses on structures (PATHS) computational model, which can solve many PBPs, is presented and compared to human performance on the same problems. PATHS and humans are similarly affected by the ordering of scenes within a PBP. Spatially or temporally juxtaposing similar (relative to dissimilar) scenes promotes category learning when the scenes belong to different categories but hinders learning when the similar scenes belong to the same category. The core theoretical commitments of PATHS, which we believe to also exemplify open-ended human category learning, are (a) the continual perception of new scene descriptions over the course of category learning; (b) the context-dependent nature of that perceptual process, in which the perceived scenes establish the context for the perception of subsequent scenes; (c) hypothesis construction by combining descriptions into explicit rules; and (d) bidirectional interactions between perceiving new aspects of scenes and constructing hypotheses for the rule that distinguishes categories. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

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

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    Lach LM, Haschke R, Tateo D, et al. Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot. In: TouchProcessing workshop @ NeurIPS. 2023

    Bio-Inspired Grasping Controller for Sensorized 2-DoF Grippers

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    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

    Nucleus basalis of Meynert predicts cognition after deep brain stimulation in Parkinson's disease

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    INTRODUCTION Subthalamic DBS in Parkinson's disease has been associated with cognitive decline in few cases. Volume reduction of the nucleus basalis of Meynert (NBM) seems to precede cognitive impairment in Parkinson's disease. In this retrospective study, we evaluated NBM volume as a predictor of cognitive outcome 1 year after subthalamic DBS. METHODS NBM volumes were calculated from preoperative MRIs using voxel-based morphometry. Cognitive outcome was defined as the relative change of MMSE or DemTect scores from pre-to 1 year postoperatively. A multiple linear regression analysis adjusted for the number of cognitive domains affected in the preoperative neuropsychological testing and UPDRS III was conducted. To account for other variables and potential non-linear effects, an additional machine learning analysis using random forests was applied. RESULTS 55 patients with Parkinson's disease (39 male, age 61.4¬†¬Ī¬†7.5 years, disease duration 10.8¬†¬Ī¬†4.7 years) who received bilateral subthalamic DBS electrodes at our center were included. Although overall cognition did not change significantly, individual change in cognitive abilities was variable. Cognitive outcome could be predicted based on NBM size (B¬†=¬†208.98, p¬†=¬†0.022*) in the regression model (F(3,49)¬†=¬†2.869; R2 of 0.149; p¬†=¬†0.046*). Using random forests with more variables, cognitive outcome could also be predicted (average root mean squared error between predicted and true cognitive change 11.28¬†¬Ī¬†9.51, p¬†=¬†0.039*). Also in this model, NBM volume was the most predictive variable. CONCLUSION NBM volume can be used as a simple non-invasive predictor for cognitive outcome after DBS in Parkinson's disease, especially when combined with other clinical parameters that are prognostically relevant

    Bio-Inspired Grasping Controller for Sensorized 2-DoF Grippers

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    Lach LM, Lemaignan S, Ferro F, Ritter H, Haschke R. Bio-Inspired Grasping Controller for Sensorized 2-DoF Grippers. In: Proceedings IROS. 2022.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 displace- ment 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
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