The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrations

• URL: http://arxiv.org/abs/2407.13432v3
• Date: Wed, 23 Oct 2024 08:07:05 GMT
• Title: The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrations
• Authors: Jan Ole von Hartz, Tim Welschehold, Abhinav Valada, Joschka Boedecker,
• Abstract summary: There are several open challenges to applying TP-GMMs in the wild. We factorize the robot's end-effector velocity into its direction and magnitude. We then segment and sequence skills from complex demonstration trajectories. Our approach enables learning complex manipulation tasks from just five demonstrations.
• Score: 13.747258771184372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Task Parametrized Gaussian Mixture Models (TP-GMM) are a sample-efficient method for learning object-centric robot manipulation tasks. However, there are several open challenges to applying TP-GMMs in the wild. In this work, we tackle three crucial challenges synergistically. First, end-effector velocities are non-Euclidean and thus hard to model using standard GMMs. We thus propose to factorize the robot's end-effector velocity into its direction and magnitude, and model them using Riemannian GMMs. Second, we leverage the factorized velocities to segment and sequence skills from complex demonstration trajectories. Through the segmentation, we further align skill trajectories and hence leverage time as a powerful inductive bias. Third, we present a method to automatically detect relevant task parameters per skill from visual observations. Our approach enables learning complex manipulation tasks from just five demonstrations while using only RGB-D observations. Extensive experimental evaluations on RLBench demonstrate that our approach achieves state-of-the-art performance with 20-fold improved sample efficiency. Our policies generalize across different environments, object instances, and object positions, while the learned skills are reusable.

Related papers

• MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning [17.437573206368494]
  Visual deep reinforcement learning (RL) enables robots to acquire skills from visual input for unstructured tasks. Current algorithms suffer from low sample efficiency, limiting their practical applicability. We present MENTOR, a method that improves both the architecture and optimization of RL agents.
  arXiv  Detail & Related papers  (2024-10-19T04:31:54Z)
• Affordance-based Robot Manipulation with Flow Matching [6.863932324631107]
  Our framework seamlessly unifies affordance model learning and trajectory generation with flow matching for robot manipulation. Our evaluation highlights that the proposed prompt tuning method for learning manipulation affordance with language prompter achieves competitive performance. Our framework seamlessly unifies affordance model learning and trajectory generation with flow matching for robot manipulation.
  arXiv  Detail & Related papers  (2024-09-02T09:11:28Z)
• Offline Imitation Learning Through Graph Search and Retrieval [57.57306578140857]
  Imitation learning is a powerful machine learning algorithm for a robot to acquire manipulation skills. We propose GSR, a simple yet effective algorithm that learns from suboptimal demonstrations through Graph Search and Retrieval. GSR can achieve a 10% to 30% higher success rate and over 30% higher proficiency compared to baselines.
  arXiv  Detail & Related papers  (2024-07-22T06:12:21Z)
• Deciphering Movement: Unified Trajectory Generation Model for Multi-Agent [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs. Specifically, we introduce a Ghost Spatial Masking (GSM) module embedded within a Transformer encoder for spatial feature extraction. We benchmark three practical sports game datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• DITTO: Demonstration Imitation by Trajectory Transformation [31.930923345163087]
  In this work, we address the problem of one-shot imitation from a single human demonstration, given by an RGB-D video recording. We propose a two-stage process. In the first stage we extract the demonstration trajectory offline. This entails segmenting manipulated objects and determining their relative motion in relation to secondary objects such as containers. In the online trajectory generation stage, we first re-detect all objects, then warp the demonstration trajectory to the current scene and execute it on the robot.
  arXiv  Detail & Related papers  (2024-03-22T13:46:51Z)
• Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models [13.07916598175886]
  We study the multi-task learning problem on GMMs. We propose a multi-task GMM learning procedure based on the EM algorithm. We generalize our approach to tackle the problem of transfer learning for GMMs.
  arXiv  Detail & Related papers  (2022-09-30T04:35:12Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)
• Multitask Adaptation by Retrospective Exploration with Learned World Models [77.34726150561087]
  We propose a meta-learned addressing model called RAMa that provides training samples for the MBRL agent taken from task-agnostic storage. The model is trained to maximize the expected agent's performance by selecting promising trajectories solving prior tasks from the storage.
  arXiv  Detail & Related papers  (2021-10-25T20:02:57Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Learning to Shift Attention for Motion Generation [55.61994201686024]
  One challenge of motion generation using robot learning from demonstration techniques is that human demonstrations follow a distribution with multiple modes for one task query. Previous approaches fail to capture all modes or tend to average modes of the demonstrations and thus generate invalid trajectories. We propose a motion generation model with extrapolation ability to overcome this problem.
  arXiv  Detail & Related papers  (2021-02-24T09:07:52Z)
• Meta Adaptation using Importance Weighted Demonstrations [19.37671674146514]
  In some cases, the distribution shifts, so much, that it is difficult for an agent to infer the new task. We propose a novel algorithm to generalize on any related task by leveraging prior knowledge on a set of specific tasks. We show experiments where the robot is trained from a diversity of environmental tasks and is also able to adapt to an unseen environment.
  arXiv  Detail & Related papers  (2019-11-23T07:22:32Z)

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.