A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures

• URL: http://arxiv.org/abs/2107.01829v1
• Date: Mon, 5 Jul 2021 07:37:17 GMT
• Title: A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures
• Authors: Yoojin Oh, Marc Toussaint, Jim Mainprice
• Abstract summary: This paper presents a teleoperation system that includes robot perception and intent prediction from hand gestures. The perception module identifies the objects present in the robot workspace and the intent prediction module which object the user likely wants to grasp.
• Score: 20.120263332724438
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents a teleoperation system that includes robot perception and intent prediction from hand gestures. The perception module identifies the objects present in the robot workspace and the intent prediction module which object the user likely wants to grasp. This architecture allows the approach to rely on traded control instead of direct control: we use hand gestures to specify the goal objects for a sequential manipulation task, the robot then autonomously generates a grasping or a retrieving motion using trajectory optimization. The perception module relies on the model-based tracker to precisely track the 6D pose of the objects and makes use of a state of the art learning-based object detection and segmentation method, to initialize the tracker by automatically detecting objects in the scene. Goal objects are identified from user hand gestures using a trained a multi-layer perceptron classifier. After presenting all the components of the system and their empirical evaluation, we present experimental results comparing our pipeline to a direct traded control approach (i.e., one that does not use prediction) which shows that using intent prediction allows to bring down the overall task execution time.

Related papers

• Articulated Object Manipulation using Online Axis Estimation with SAM2-Based Tracking [59.87033229815062]
  Articulated object manipulation requires precise object interaction, where the object's axis must be carefully considered. Previous research employed interactive perception for manipulating articulated objects, but typically, open-loop approaches often suffer from overlooking the interaction dynamics. We present a closed-loop pipeline integrating interactive perception with online axis estimation from segmented 3D point clouds.
  arXiv  Detail & Related papers  (2024-09-24T17:59:56Z)
• Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation [65.46610405509338]
  We seek to learn a generalizable goal-conditioned policy that enables zero-shot robot manipulation. Our framework,Track2Act predicts tracks of how points in an image should move in future time-steps based on a goal. We show that this approach of combining scalably learned track prediction with a residual policy enables diverse generalizable robot manipulation.
  arXiv  Detail & Related papers  (2024-05-02T17:56:55Z)
• SeMoLi: What Moves Together Belongs Together [51.72754014130369]
  We tackle semi-supervised object detection based on motion cues. Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
  arXiv  Detail & Related papers  (2024-02-29T18:54:53Z)
• Intention estimation from gaze and motion features for human-robot shared-control object manipulation [1.128708201885454]
  Shared control can help in teleoperated object manipulation by assisting with the execution of the user's intention. An intention estimation framework is presented, which uses natural gaze and motion features to predict the current action and the target object.
  arXiv  Detail & Related papers  (2022-08-18T07:53:19Z)
• Object Manipulation via Visual Target Localization [64.05939029132394]
  Training agents to manipulate objects, poses many challenges. We propose an approach that explores the environment in search for target objects, computes their 3D coordinates once they are located, and then continues to estimate their 3D locations even when the objects are not visible. Our evaluations show a massive 3x improvement in success rate over a model that has access to the same sensory suite.
  arXiv  Detail & Related papers  (2022-03-15T17:59:01Z)
• Task-Focused Few-Shot Object Detection for Robot Manipulation [1.8275108630751844]
  We develop a manipulation method based solely on detection then introduce task-focused few-shot object detection to learn new objects and settings. In experiments for our interactive approach to few-shot learning, we train a robot to manipulate objects directly from detection (ClickBot)
  arXiv  Detail & Related papers  (2022-01-28T21:52:05Z)
• Trajectory-based Reinforcement Learning of Non-prehensile Manipulation Skills for Semi-Autonomous Teleoperation [18.782289957834475]
  We present a semi-autonomous teleoperation framework for a pick-and-place task using an RGB-D sensor. A trajectory-based reinforcement learning is utilized for learning the non-prehensile manipulation to rearrange the objects. We show that the proposed method outperforms manual keyboard control in terms of the time duration for the grasping.
  arXiv  Detail & Related papers  (2021-09-27T14:27:28Z)
• Neural Network Based Lidar Gesture Recognition for Realtime Robot Teleoperation [0.0]
  We propose a novel low-complexity lidar gesture recognition system for mobile robot control. The system is lightweight and suitable for mobile robot control with limited computing power. The use of lidar contributes to the robustness of the system, allowing it to operate in most outdoor conditions.
  arXiv  Detail & Related papers  (2021-09-17T00:49:31Z)
• Dynamic Modeling of Hand-Object Interactions via Tactile Sensing [133.52375730875696]
  In this work, we employ a high-resolution tactile glove to perform four different interactive activities on a diversified set of objects. We build our model on a cross-modal learning framework and generate the labels using a visual processing pipeline to supervise the tactile model. This work takes a step on dynamics modeling in hand-object interactions from dense tactile sensing.
  arXiv  Detail & Related papers  (2021-09-09T16:04:14Z)
• "What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences [27.915309216800125]
  We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. We propose a single, end-to-end trainable architecture which jointly incorporates motion cues and semantic knowledge. Our method neither depends on any visual registration of a kinematic robot or 3D object models, nor on precise hand-eye calibration or any additional sensor data.
  arXiv  Detail & Related papers  (2020-11-06T10:55:28Z)
• Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives [89.34229413345541]
  We propose a conditioning scheme which avoids pitfalls by learning the controller and its conditioning in an end-to-end manner. Our model predicts complex action sequences based directly on a dynamic image representation of the robot motion. We report significant improvements in task success over representative MPC and IL baselines.
  arXiv  Detail & Related papers  (2020-03-19T15:04:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.