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In the human hand, high-density contact information provided by afferent neurons is essential for many human grasping and manipulation capabilities. In contrast, robotic tactile sensors, including the state-of-the-art SynTouch BioTac, are typically used to provide low-density contact information, such as contact location, center of pressure, and net force. Although useful, these data do not convey or leverage the rich information content that some tactile sensors naturally measure. This research extends robotic tactile sensing beyond reduced-order models through (1) the automated creation of a precise experimental tactile dataset for the BioTac over a diverse range of physical interactions, (2) a 3D finite-element (FE) model of the BioTac, which complements the experimental dataset with high-density, distributed contact data, (3) neural-network-based mappings from raw BioTac signals to not only low-dimensional experimental data, but also high-density FE deformation fields, and (4) mappings from the FE deformation fields to the raw signals themselves. The high-density data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible. Datasets, CAD files for the experimental testbed, FE model files, and videos are available at https://sites.google.com/nvidia.com/tactiledata .

Truth finds itself under threat. That it is under threat at all is not new. Yet with the advent of new technologies, particularly those that allow for varying modes of being and expression, informa...

This article presents a set of performance metrics, test methods, and associated artifacts to help progress the development and deployment of robotic assembly systems. The designs for three task board artifacts that replicate small part insertion and fastening operations such as threading, snap fitting, and meshing with standard screws, nuts, washers, gears, electrical connectors, belt drives, and wiring are presented. To support the evaluation of robotic assembly and disassembly operations, benchmarking protocols and performance metrics are presented that leverage these task boards. Finally, robot competitions are discussed as use cases for these task boards.

South African academic and historian Arthur Keppel-Jones wrote When Smuts Goes as an imagined history of a time (1952 to 2010) when fascist Afrikaner nationalism consumed the country. Keppel-Jones’ book was published in 1947; apartheid began in 1948. This article considers previous scholarship on Keppel-Jones’ work (such as its depictions of race), before beginning an analysis on the text’s temporal forms. The work has also been discussed as a dystopic text, and I use this to introduce my primary concerns: the playful temporal forms apparent in the narrative’s telling, where this future is told as if a historical account. Derek Hook’s understanding of apartheid time is relied upon throughout the latter half of the essay, where the article argues that the text’s multiple temporal expressions may be explained by, and perhaps even expand upon, our understanding of the logic of apartheid temporality. Finally, I draw upon the shortcomings of using a theory of psychoanalytical temporality that looks back (Hook’s formulation of the theory, specifically), to read a work that looks forward.

We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough benchmark of state-of-the-art approaches on three relevant tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand pose estimation. Finally, we evaluate a new robotics-relevant task: generating safe robot grasps in human-to-robot object handover. Dataset and code are available at https://dex-ycb.github.io., Accepted to CVPR 2021

We consider the problem of learning motion policies for acceleration-based robotics systems with a structured policy class. We leverage a multi-task control framework called RMPflow which has been successfully applied in many robotics problems. Using RMPflow as a structured policy class in learning has several benefits, such as sufficient expressiveness, the flexibility to inject different levels of prior knowledge as well as the ability to transfer policies between robots. However, implementing a system for end-to-end learning of RMPflow policies faces several computational challenges. In this work, we re-examine the RMPflow algorithm and propose a more practical alternative, called RMP2, that uses modern automatic differentiation tools (such as TensorFlow and PyTorch) to compute RMPflow policies. Our new design retains the strengths of RMPflow while bringing in advantages from automatic differentiation, including 1) simple programming interfaces to designing complex transformations; 2) support of general directed acyclic graph (DAG) transformation structures; 3) end-to-end differentiability for policy learning; 4) improved computational efficiency. Because of these features, RMP2 can be treated as a structured policy class for efficient robot learning that is suitable for encoding domain knowledge. Our experiments show that using the structured policy class given by RMP2 can improve policy performance and safety in reinforcement learning tasks for goal reaching in cluttered space. The video for our experimental results can be found at https://youtu.be/dliQ-jsYhgI and the code is available at https://github.com/UWRobotLearning/rmp2.

Part 3: Assembly Operations; International audience; A peg-in-hole operation is representative of common tasks performed in assembly lines by robots. It requires registration of the coordinate frame where a part resides to the robot frame where it is acted upon. Poor registration causes misalignment of the peg and the hole which will result in a failed insertion of the peg. In this study, the dependence of the outcome of the insertion task on the quality of registration is investigated. It was shown in earlier studies that Restoring Rigid-Body Condition (RRBC) leads to decreased target registration error. This study quantifies the improvement in the Pass/Fail rate when the target registration error is reduced for a given peg-hole tolerance. A robot arm operated in position control mode was used to perform the insertion without performing any search algorithms or force/torque sensing. Results show that use of the RRBC method yielded substantial reduction in failed insertions, especially for tighter tolerances.

Robotics research has found numerous important applications of Riemannian geometry. Despite that, the concept remain challenging to many roboticists because the background material is complex and strikingly foreign. Beyond {\em Riemannian} geometry, there are many natural generalizations in the mathematical literature -- areas such as Finsler geometry and spray geometry -- but those generalizations are largely inaccessible, and as a result there remain few applications within robotics. This paper presents a re-derivation of spray and Finsler geometries we found critical for the development of our recent work on a powerful behavioral design tool we call geometric fabrics. These derivations build from basic tools in advanced calculus and the calculus of variations making them more accessible to a robotics audience than standard presentations. We focus on the pragmatic and calculable results, avoiding the use of tensor notation to appeal to a broader audience, emphasizing geometric path consistency over ideas around connections and curvature. We hope that these derivations will contribute to an increased understanding of generalized nonlinear, and even classical Riemannian, geometry within the robotics community and inspire future research into new applications.