Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots

• URL: http://arxiv.org/abs/2108.03150v1
• Date: Fri, 6 Aug 2021 14:45:57 GMT
• Title: Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots
• Authors: Sim\'on C. Smith, Subramanian Ramamoorthy
• Abstract summary: A performance function gives us insights into the behaviour of the robot. In high-dimensionality systems, where the actionstate space is large, fine-tuning a controller is non-trivial. We propose a performance function whose domain is defined by external features and parameters of the controller.
• Score: 8.147652597876862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding a controller's performance in different scenarios is crucial for robots that are going to be deployed in safety-critical tasks. If we do not have a model of the dynamics of the world, which is often the case in complex domains, we may need to approximate a performance function of the robot based on its interaction with the environment. Such a performance function gives us insights into the behaviour of the robot, allowing us to fine-tune the controller with manual interventions. In high-dimensionality systems, where the actionstate space is large, fine-tuning a controller is non-trivial. To overcome this problem, we propose a performance function whose domain is defined by external features and parameters of the controller. Attainment regions are defined over such a domain defined by feature-parameter pairs, and serve the purpose of enabling prediction of successful execution of the task. The use of the feature-parameter space -in contrast to the action-state space- allows us to adapt, explain and finetune the controller over a simpler (i.e., lower dimensional space). When the robot successfully executes the task, we use the attainment regions to gain insights into the limits of the controller, and its robustness. When the robot fails to execute the task, we use the regions to debug the controller and find adaptive and counterfactual changes to the solutions. Another advantage of this approach is that we can generalise through the use of Gaussian processes regression of the performance function in the high-dimensional space. To test our approach, we demonstrate learning an approximation to the performance function in simulation, with a mobile robot traversing different terrain conditions. Then, with a sample-efficient method, we propagate the attainment regions to a physical robot in a similar environment.

Related papers

• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• Volumetric Ergodic Control [0.0]
  We introduce a new ergodic control formulation that optimize spatial coverage using a volumetric state representation.<n>Our method preserves the coverage guarantees of ergodic control, adds minimal computational overhead for real-time control, and supports arbitrary sample-based volumetric models.
  arXiv  Detail & Related papers  (2025-11-14T18:10:40Z)
• Action Flow Matching for Continual Robot Learning [57.698553219660376]
  Continual learning in robotics seeks systems that can constantly adapt to changing environments and tasks. We introduce a generative framework leveraging flow matching for online robot dynamics model alignment. We find that by transforming the actions themselves rather than exploring with a misaligned model, the robot collects informative data more efficiently.
  arXiv  Detail & Related papers  (2025-04-25T16:26:15Z)
• A Real-time Anomaly Detection Method for Robots based on a Flexible and Sparse Latent Space [2.0186752447895993]
  Deep learning-based models in robotics face challenges due to limited training data and highly noisy signal features. We present Sparse Masked Autoregressive Flow-based Adversarial AutoEncoders model to address these problems. Our model performs inferences within 1 millisecond, ensuring real-time anomaly detection.
  arXiv  Detail & Related papers  (2025-04-15T13:17:14Z)
• ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation [58.615616224739654]
  Conventional robotic manipulation methods usually learn semantic representation of the observation for prediction. We propose a dynamic Gaussian Splatting method named ManiGaussian for multi-temporal robotic manipulation. Our framework can outperform the state-of-the-art methods by 13.1% in average success rate.
  arXiv  Detail & Related papers  (2024-03-13T08:06:41Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Learning to navigate efficiently and precisely in real environments [14.52507964172957]
  Embodied AI literature focuses on end-to-end agents trained in simulators like Habitat or AI-Thor. In this work we explore end-to-end training of agents in simulation in settings which minimize the sim2real gap.
  arXiv  Detail & Related papers  (2024-01-25T17:50:05Z)
• AI planning in the imagination: High-level planning on learned abstract search spaces [68.75684174531962]
  We propose a new method, called PiZero, that gives an agent the ability to plan in an abstract search space that the agent learns during training. We evaluate our method on multiple domains, including the traveling salesman problem, Sokoban, 2048, the facility location problem, and Pacman.
  arXiv  Detail & Related papers  (2023-08-16T22:47:16Z)
• Obstacle Avoidance for Robotic Manipulator in Joint Space via Improved Proximal Policy Optimization [6.067589886362815]
  In this paper, we train a deep neural network via an improved Proximal Policy Optimization (PPO) algorithm to map from task space to joint space for a 6-DoF manipulator. Since training such a task in real-robot is time-consuming and strenuous, we develop a simulation environment to train the model. Experimental results showed that using our method, the robot was capable of tracking a single target or reaching multiple targets in unstructured environments.
  arXiv  Detail & Related papers  (2022-10-03T10:21:57Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space [1.4824891788575418]
  We present an integrated multi-robot task-motion planning framework for navigation in knowledge-intensive domains. In particular, we consider a distributed multi-robot setting incorporating mutual observations between the robots. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning.
  arXiv  Detail & Related papers  (2020-10-01T06:45:17Z)

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.