Learning to Recover from Plan Execution Errors during Robot Manipulation: A Neuro-symbolic Approach

• URL: http://arxiv.org/abs/2405.18948v1
• Date: Wed, 29 May 2024 10:03:57 GMT
• Title: Learning to Recover from Plan Execution Errors during Robot Manipulation: A Neuro-symbolic Approach
• Authors: Namasivayam Kalithasan, Arnav Tuli, Vishal Bindal, Himanshu Gaurav Singh, Parag Singla, Rohan Paul,
• Abstract summary: We propose an approach (blending learning with symbolic search) for automated error discovery and recovery. We present an anytime version of our algorithm, where instead of recovering to the last correct state, we search for a sub-goal in the original plan.
• Score: 7.768747914019512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatically detecting and recovering from failures is an important but challenging problem for autonomous robots. Most of the recent work on learning to plan from demonstrations lacks the ability to detect and recover from errors in the absence of an explicit state representation and/or a (sub-) goal check function. We propose an approach (blending learning with symbolic search) for automated error discovery and recovery, without needing annotated data of failures. Central to our approach is a neuro-symbolic state representation, in the form of dense scene graph, structured based on the objects present within the environment. This enables efficient learning of the transition function and a discriminator that not only identifies failures but also localizes them facilitating fast re-planning via computation of heuristic distance function. We also present an anytime version of our algorithm, where instead of recovering to the last correct state, we search for a sub-goal in the original plan minimizing the total distance to the goal given a re-planning budget. Experiments on a physics simulator with a variety of simulated failures show the effectiveness of our approach compared to existing baselines, both in terms of efficiency as well as accuracy of our recovery mechanism.

Related papers

• Model Checking for Closed-Loop Robot Reactive Planning [0.0]
  We show how model checking can be used to create multistep plans for a differential drive wheeled robot so that it can avoid immediate danger. Using a small, purpose built model checking algorithm in situ we generate plans in real-time in a way that reflects the egocentric reactive response of simple biological agents.
  arXiv  Detail & Related papers  (2023-11-16T11:02:29Z)
• Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN [77.0623472106488]
  In this paper, we explore a class of distributional instance segmentation models using latent codes. For robotic picking applications, we propose a confidence mask method to achieve the high precision necessary. We show that our method can significantly reduce critical errors in robotic systems, including our newly released dataset of ambiguous scenes.
  arXiv  Detail & Related papers  (2023-05-03T05:57:29Z)
• Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network [63.8376359764052]
  Modern robotic platforms need a reliable localization system to operate daily beside humans. Simple pose estimation algorithms based on filtered wheel and inertial odometry often fail in the presence of abrupt kinematic changes and wheel slips. We propose an innovative online learning approach for wheel odometry correction, paving the way for a robust multi-source localization system.
  arXiv  Detail & Related papers  (2023-03-21T10:30:31Z)
• Optimal decision making in robotic assembly and other trial-and-error tasks [1.0660480034605238]
  We study a class of problems providing (1) low-entropy indicators of terminal success / failure, and (2) unreliable (high-entropy) data to predict the final outcome of an ongoing task. We derive a closed form solution that predicts makespan based on the confusion matrix of the failure predictor. This allows the robot to learn failure prediction in a production environment, and only adopt a preemptive policy when it actually saves time.
  arXiv  Detail & Related papers  (2023-01-25T22:07:50Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)
• Low Dimensional State Representation Learning with Reward-shaped Priors [7.211095654886105]
  We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space. This mapping is learned with unsupervised learning using loss functions shaped to incorporate prior knowledge of the environment and the task. We test the method on several mobile robot navigation tasks in a simulation environment and also on a real robot.
  arXiv  Detail & Related papers  (2020-07-29T13:00:39Z)
• Exploiting Ergonomic Priors in Human-to-Robot Task Transfer [3.60953887026184]
  A method based on programming by demonstration is proposed to learn null space policies from constrained motion data. The effectiveness of the method has been demonstrated in a 3-link simulation and a real world experiment using a human subject as the demonstrator. The approach is shown to outperform the current state-of-the-art approach in a simulated 3DoF robot manipulator control problem.
  arXiv  Detail & Related papers  (2020-03-01T18:30:57Z)

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.