Learning robot in-hand manipulation with tactile features

Dexterous manipulation enables repositioning of objects and tools within a robot’s hand. When applying dexterous manipulation to unknown objects, exact object models are not available. Instead of relying on models, compliance and tactile feedback can be exploited to adapt to unknown objects. However, compliant hands and tactile sensors add complexity and are themselves difficult to model. Hence, we propose acquiring in-hand manipulation skills through reinforcement learning, which does not require analytic dynamics or kinematics models. In this paper, we show that this approach successfully acquires a tactile manipulation skill using a passively compliant hand. Additionally, we show that the learned tactile skill generalizes to novel objects

Relevance of motion-related assessment metrics in laparoscopic surgery

INTRODUCTION: Motion metrics have become an important source of information when addressing the assessment of surgical expertise. However, their direct relationship with the different surgical skills has not been fully explored. The purpose of this study is to investigate the relevance of motion-related metrics in the evaluation processes of basic psychomotor laparoscopic skills, as well as their correlation with the different abilities sought to measure. METHODS: A framework for task definition and metric analysis is proposed. An explorative survey was first conducted with a board of experts to identify metrics to assess basic psychomotor skills. Based on the output of that survey, three novel tasks for surgical assessment were designed. Face and construct validation study was performed, with focus on motion-related metrics. Tasks were performed by 42 participants (16 novices, 22 residents and 4 experts). Movements of the laparoscopic instruments were registered with the TrEndo tracking system and analyzed. RESULTS: Time, path length and depth showed construct validity for all three tasks. Motion smoothness and idle time also showed validity for tasks involving bi-manual coordination and tasks requiring a more tactical approach respectively. Additionally, motion smoothness and average speed showed a high internal consistency, proving them to be the most task-independent of all the metrics analyzed. CONCLUSION: Motion metrics are complementary and valid for assessing basic psychomotor skills, and their relevance depends on the skill being evaluated. A larger clinical implementation, combined with quality performance information, will give more insight on the relevance of the results shown in this study

Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks

We outline a possible theoretical framework for the quantitative modeling of networked embodied cognitive systems. We notice that: 1) information self structuring through sensory-motor coordination does not deterministically occur in Rn vector space, a generic multivariable space, but in SE(3), the group structure of the possible motions of a body in space; 2) it happens in a stochastic open ended environment. These observations may simplify, at the price of a certain abstraction, the modeling and the design of self organization processes based on the maximization of some informational measures, such as mutual information. Furthermore, by providing closed form or computationally lighter algorithms, it may significantly reduce the computational burden of their implementation. We propose a modeling framework which aims to give new tools for the design of networks of new artificial self organizing, embodied and intelligent agents and the reverse engineering of natural ones. At this point, it represents much a theoretical conjecture and it has still to be experimentally verified whether this model will be useful in practice.

Dexterous Soft Hands Linearize Feedback-Control for In-Hand Manipulation

This paper presents a feedback-control framework for in-hand manipulation (IHM) with dexterous soft hands that enables the acquisition of manipulation skills in the real-world within minutes. We choose the deformation state of the soft hand as the control variable. To control for a desired deformation state, we use coarsely approximated Jacobians of the actuation-deformation dynamics. These Jacobian are obtained via explorative actions. This is enabled by the self-stabilizing properties of compliant hands, which allow us to use linear feedback control in the presence of complex contact dynamics. To evaluate the effectiveness of our approach, we show the generalization capabilities for a learned manipulation skill to variations in object size by 100 %, 360 degree changes in palm inclination and to disabling up to 50 % of the involved actuators. In addition, complex manipulations can be obtained by sequencing such feedback-skills.Comment: Accepted at 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Active haptic perception in robots: a review

In the past few years a new scenario for robot-based applications has emerged. Service and mobile robots have opened new market niches. Also, new frameworks for shop-floor robot applications have been developed. In all these contexts, robots are requested to perform tasks within open-ended conditions, possibly dynamically varying. These new requirements ask also for a change of paradigm in the design of robots: on-line and safe feedback motion control becomes the core of modern robot systems. Future robots will learn autonomously, interact safely and possess qualities like self-maintenance. Attaining these features would have been relatively easy if a complete model of the environment was available, and if the robot actuators could execute motion commands perfectly relative to this model. Unfortunately, a complete world model is not available and robots have to plan and execute the tasks in the presence of environmental uncertainties which makes sensing an important component of new generation robots. For this reason, today\u2019s new generation robots are equipped with more and more sensing components, and consequently they are ready to actively deal with the high complexity of the real world. Complex sensorimotor tasks such as exploration require coordination between the motor system and the sensory feedback. For robot control purposes, sensory feedback should be adequately organized in terms of relevant features and the associated data representation. In this paper, we propose an overall functional picture linking sensing to action in closed-loop sensorimotor control of robots for touch (hands, fingers). Basic qualities of haptic perception in humans inspire the models and categories comprising the proposed classification. The objective is to provide a reasoned, principled perspective on the connections between different taxonomies used in the Robotics and human haptic literature. The specific case of active exploration is chosen to ground interesting use cases. Two reasons motivate this choice. First, in the literature on haptics, exploration has been treated only to a limited extent compared to grasping and manipulation. Second, exploration involves specific robot behaviors that exploit distributed and heterogeneous sensory data

LocaliseBot: Multi-view 3D object localisation with differentiable rendering for robot grasping

Robot grasp typically follows five stages: object detection, object localisation, object pose estimation, grasp pose estimation, and grasp planning. We focus on object pose estimation. Our approach relies on three pieces of information: multiple views of the object, the camera's extrinsic parameters at those viewpoints, and 3D CAD models of objects. The first step involves a standard deep learning backbone (FCN ResNet) to estimate the object label, semantic segmentation, and a coarse estimate of the object pose with respect to the camera. Our novelty is using a refinement module that starts from the coarse pose estimate and refines it by optimisation through differentiable rendering. This is a purely vision-based approach that avoids the need for other information such as point cloud or depth images. We evaluate our object pose estimation approach on the ShapeNet dataset and show improvements over the state of the art. We also show that the estimated object pose results in 99.65% grasp accuracy with the ground truth grasp candidates on the Object Clutter Indoor Dataset (OCID) Grasp dataset, as computed using standard practice

Grasp planning under uncertainty

Advanced robots such as mobile manipulators offer nowadays great opportunities for realistic manipulators. Physical interaction with the environment is an essential capability for service robots when acting in unstructured environments such as homes. Thus, manipulation and grasping under uncertainty has become a critical research area within robotics research. This thesis explores techniques for a robot to plan grasps in presence of uncertainty in knowledge about objects such as their pose and shape. First, the question how much information about the graspable object the robot can perceive from a single tactile exploration attempt is considered. Next, a tactile-based probabilistic approach for grasping which aims to maximize the probability of a successful grasp is presented. The approach is further extended to include information gathering actions based on maximal entropy reduction. The combined framework unifies ideas behind planning for maximally stable grasps, the possibilities of sensor-based grasping and exploration. Another line of research is focused on grasping familiar object belonging to a specific category. Moreover, the task is also included in the planning process as in many applications the resulting grasp should be not only stable but task compatible. The vision-based framework takes the idea of maximizing grasp stability in the novel context to cover shape uncertainty. Finally, the RGB-D vision-based probabilistic approach is extended to include tactile sensor feedback in the control loop to incrementally improve estimates about object shape and pose and then generate more stable task compatible grasps. The results of the studies demonstrate the benefits of applying probabilistic models and using different sensor measurements in grasp planning and prove that this is a promising direction of study and research. Development of such approaches, first of all, contributes to the rapidly developing area of household applications and service robotics