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2024 International Conference on Intelligent robots and Systems

作者： Kasolowsky, Ulf Baeuml, Berthold Tech Univ Munich Learning AI Dextrous Robots Lab Munich Germany German Aerosp Ctr DLR Inst Robot & Mechatron Cologne Germany

ISBN: (纸本)9798350377712;9798350377705

We want to enable fine manipulation with a multi-fingered robotic hand by using modern deep reinforcement learning methods. Key for fine manipulation is a spatially resolved tactile sensor. Here, we present a novel model of a tactile skin that can be used together with rigid-body (hence fast) physics simulators. The model considers the softness of the real fingertips such that a contact can spread across multiple taxels of the sensor depending on the contact geometry. We calibrate the model parameters to allow for an accurate simulation of the real-world sensor. For this, we present a self-contained calibration method without external tools or sensors. To demonstrate the validity of our approach, we learn two challenging fine manipulation tasks: Rolling a marble and a bolt between two fingers. We show in simulation experiments that tactile feedback is crucial for precise manipulation and reaching sub-taxel resolution of <1 mm (despite a taxel spacing of 4 mm). Moreover, we demonstrate that all policies successfully transfer from the simulation to the real robotic hand. Website: ***/skin/iros24

关键词： Robotic arms

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A learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand

A Learning-based Controller for Multi-Contact Grasps on Unkn...

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2024 International Conference on Intelligent robots and Systems

作者： Winkelbauer, Dominik Triebel, Rudolph Baeuml, Berthold DLR Inst Robot & Mechatron Wessling Germany Tech Univ Munich Learning AI Dextrous Robots Lab Munich Germany Karlsruhe Inst Technol KIT Karlsruhe Germany

ISBN: (纸本)9798350377712;9798350377705

Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbitrary grasp types, including power grasps with multi-contacts, while operating self-contained on before unseen objects. No detailed contact information is needed, but only a rough 3D model, e.g., reconstructed from a single depth image. First, the external wrench being applied to the object is estimated by using the measured torques at the joints. Then, the torques necessary to counteract the estimated wrench while keeping the object at its initial pose are predicted. The torques are commanded via desired joint angles to an underlying joint-level impedance controller. To reach real-time performance, we propose a learning-based approach that is based on a wrench estimator- and a torque predictor neural network. Both networks are trained in a supervised fashion using data generated via the analytical formulation of the controller. In an extensive simulation-based evaluation, we show that our controller is able to keep 83.1% of the tested grasps stable when applying external wrenches with up to 10 N. At the same time, we outperform the two tested baselines by being more efficient and inducing less involuntary object movement. Finally, we show that the controller also works on the real DLR-Hand II, reaching a cycle time of 6ms. Website: ***/grasping

关键词： 3D reconstruction

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learning Time-Optimal and Speed-Adjustable Tactile In-Hand Manipulation

arXiv

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arXiv 2024年

作者： Pitz, Johannes Röstel, Lennart Sievers, Leon Bäuml, Berthold Learning AI for Dextrous Robots Lab Technical University of Munich Germany Germany

In-hand manipulation with multi-fingered hands is a challenging problem that recently became feasible with the advent of deep reinforcement learning methods. While most contributions to the task brought improvements in robustness and generalization, this paper addresses the critical performance measure of the speed at which an in-hand manipulation can be performed. We present reinforcement learning policies that can perform in-hand reorientation significantly faster than previous approaches for the complex setting of goal-conditioned reorientation in SO(3) with permanent force closure and tactile feedback only (i.e., using the hand’s torque and position sensors). Moreover, we show how policies can be trained to be speed-adjustable, allowing for setting the average orientation speed of the manipulated object during deployment. To this end, we present suitable and minimalistic reinforcement learning objectives for time-optimal and speed-adjustable in-hand manipulation, as well as an analysis based on extensive experiments in simulation. We also demonstrate the zero-shot transfer of the learned policies to the real DLR-Hand II with a wide range of target speeds and the fastest dextrous in-hand manipulation without visual inputs. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects

Learning a Shape-Conditioned Agent for Purely Tactile In-Han...

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2024 International Conference on Intelligent robots and Systems

作者： Pitz, Johannes Roestel, Lennart Sievers, Leon Burschka, Darius Baeuml, Berthold Tech Univ Munich Learning AI Dextrous Robots Lab Munich Germany German Aerosp Ctr DLR Inst Robot & Mechatron Munich Germany Tech Univ Munich Machine Vis & Percept Grp Munich Germany

ISBN: (纸本)9798350377712;9798350377705

Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent supervision of the object state from visual sensors. This is far from human capabilities and from what is needed in real-world applications. In this work, we address this gap by training shape-conditioned agents to reorient diverse objects in hand, relying purely on tactile feedback (via torque and position measurements of the fingers' joints). To achieve this, we propose a learning framework that exploits shape information in a reinforcement learning policy and a learned state estimator. We find that representing 3D shapes by vectors from a fixed set of basis points to the shape's surface, transformed by its predicted 3D pose, is especially helpful for learning dexterous in-hand manipulation. In simulation and real-world experiments, we show the reorientation of many objects with high success rates, on par with state-of-the-art results obtained with specialized single-object agents. Moreover, we show generalization to novel objects, achieving success rates of similar to 90% even for non-convex shapes. Website: https://***/manipulation/iros24

关键词： Reinforcement learning

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Fine Manipulation Using a Tactile Skin: learning in Simulation and Sim-to-Real Transfer

Fine Manipulation Using a Tactile Skin: Learning in Simulati...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Ulf Kasolowsky Berthold Bäuml Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany DLR Institute of Robotics and Mechatronics German Aerospace Center

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

关键词： Hands Geometry Tactile sensors Fasteners Skin Calibration Spatial resolution robots Physics Intelligent robots

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learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects

arXiv

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arXiv 2024年

作者： Pitz, Johannes Röstel, Lennart Sievers, Leon Burschka, Darius Bäuml, Berthold Learning AI for Dextrous Robots Lab Technical University of Munich Germany Germany Machine Vision and Perception Group Technical University of Munich Germany

Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent supervision of the object state from visual sensors. This is far from human capabilities and from what is needed in real-world applications. In this work, we address this gap by training shape-conditioned agents to reorient diverse objects in hand, relying purely on tactile feedback (via torque and position measurements of the fingers’ joints). To achieve this, we propose a learning framework that exploits shape information in a reinforcement learning policy and a learned state estimator. We find that representing 3D shapes by vectors from a fixed set of basis points to the shape’s surface, transformed by its predicted 3D pose, is especially helpful for learning dexterous in-hand manipulation. In simulation and real-world experiments, we show the reorientation of many objects with high success rates, on par with state-of-the-art results obtained with specialized single-object agents. Moreover, we show generalization to novel objects, achieving success rates of ∼90% even for non-convex shapes. Website: https://***/manipulation/iros24 Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand

A Learning-based Controller for Multi-Contact Grasps on Unkn...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Dominik Winkelbauer Rudolph Triebel Berthold Bäuml DLR Institute of Robotics & Mechatronics Germany Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

关键词： Solid modeling Torque Three-dimensional displays Power measurement Neural networks Robot sensing systems Real-time systems Torque measurement Impedance Intelligent robots

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learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects

Learning a Shape-Conditioned Agent for Purely Tactile In-Han...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Johannes Pitz Lennart Röstel Leon Sievers Darius Burschka Berthold Bäuml Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany DLR Institute of Robotics & Mechatronics (German Aerospace Center) Machine Vision and Perception Group Technical University of Munich

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent supervision of the object state from visual sensors. This is far from human capabilities and from what is needed in real-world applications. In this work, we address this gap by training shape-conditioned agents to reorient diverse objects in hand, relying purely on tactile feedback (via torque and position measurements of the fingers’ joints). To achieve this, we propose a learning framework that exploits shape information in a reinforcement learning policy and a learned state estimator. We find that representing 3D shapes by vectors from a fixed set of basis points to the shape’s surface, transformed by its predicted 3D pose, is especially helpful for learning dexterous in-hand manipulation. In simulation and real-world experiments, we show the reorientation of many objects with high success rates, on par with state-of-the-art results obtained with specialized single-object agents. Moreover, we show generalization to novel objects, achieving success rates of ~90% even for non-convex ***: https://***/manipulation/iros24

关键词： Training Visualization Three-dimensional displays Torque Shape Tactile sensors Reinforcement learning Position measurement Vectors Sensors

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