Fine-Tuning Generative Models as an Inference Method for Robotic Tasks

• URL: http://arxiv.org/abs/2310.12862v1
• Date: Thu, 19 Oct 2023 16:11:49 GMT
• Title: Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
• Authors: Orr Krupnik, Elisei Shafer, Tom Jurgenson, Aviv Tamar
• Abstract summary: We investigate how to quickly adapt the sample generation of neural network models to observations in robotic tasks. The key idea is to quickly fine-tune the model by fitting it to generated samples matching the observed evidence. We show that our method can be applied to both autoregressive models and variational autoencoders.
• Score: 18.745665662647912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we build on recent advances in deep generative models which have greatly affected many areas of robotics. Harnessing modern GPU acceleration, we investigate how to quickly adapt the sample generation of neural network models to observations in robotic tasks. We propose a simple and general method that is applicable to various deep generative models and robotic environments. The key idea is to quickly fine-tune the model by fitting it to generated samples matching the observed evidence, using the cross-entropy method. We show that our method can be applied to both autoregressive models and variational autoencoders, and demonstrate its usability in object shape inference from grasping, inverse kinematics calculation, and point cloud completion.

Related papers

• Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control [2.058941610795796]
  This paper introduces a streamlined method for learning low-dimensional, physics-based models. We validate our approach through simulations with various planar soft manipulators. Thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.
  arXiv  Detail & Related papers  (2024-10-31T18:37:22Z)
• Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations [52.11801730860999]
  In recent years, the robot learning community has shown increasing interest in using deep generative models to capture the complexity of large datasets. We present the different types of models that the community has explored, such as energy-based models, diffusion models, action value maps, or generative adversarial networks. We also present the different types of applications in which deep generative models have been used, from grasp generation to trajectory generation or cost learning.
  arXiv  Detail & Related papers  (2024-08-08T11:34:31Z)
• Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning [0.0]
  We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios.
  arXiv  Detail & Related papers  (2024-08-07T14:32:41Z)
• Model-based Policy Optimization using Symbolic World Model [46.42871544295734]
  The application of learning-based control methods in robotics presents significant challenges. One is that model-free reinforcement learning algorithms use observation data with low sample efficiency. We suggest approximating transition dynamics with symbolic expressions, which are generated via symbolic regression.
  arXiv  Detail & Related papers  (2024-07-18T13:49:21Z)
• STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning [82.03481509373037]
  Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. We introduce Transformer-based wORld Model (STORM), an efficient world model architecture that combines strong modeling and generation capabilities. Storm achieves a mean human performance of $126.7%$ on the Atari $100$k benchmark, setting a new record among state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-14T16:42:02Z)
• 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)
• High-Accuracy Model-Based Reinforcement Learning, a Survey [2.0196229393131726]
  Deep reinforcement learning has shown remarkable success in game playing and robotics. To reduce the number of environment samples, model-based reinforcement learning creates an explicit model of the environment dynamics. Some of these methods succeed in achieving high accuracy at low sample complexity, most do so either in a robotics or in a games context.
  arXiv  Detail & Related papers  (2021-07-17T14:01:05Z)
• STAR: Sparse Transformer-based Action Recognition [61.490243467748314]
  This work proposes a novel skeleton-based human action recognition model with sparse attention on the spatial dimension and segmented linear attention on the temporal dimension of data. Experiments show that our model can achieve comparable performance while utilizing much less trainable parameters and achieve high speed in training and inference.
  arXiv  Detail & Related papers  (2021-07-15T02:53:11Z)
• Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning [42.525696463089794]
  Model Predictive Actor-Critic (MoPAC) is a hybrid model-based/model-free method that combines model predictive rollouts with policy optimization as to mitigate model bias. MoPAC guarantees optimal skill learning up to an approximation error and reduces necessary physical interaction with the environment.
  arXiv  Detail & Related papers  (2021-03-25T13:50:24Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

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.