Meta Adaptation using Importance Weighted Demonstrations
- URL: http://arxiv.org/abs/1911.10322v2
- Date: Mon, 3 Jul 2023 06:22:53 GMT
- Title: Meta Adaptation using Importance Weighted Demonstrations
- Authors: Kiran Lekkala and Sami Abu-El-Haija and Laurent Itti
- Abstract summary: 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.
- Score: 19.37671674146514
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Imitation learning has gained immense popularity because of its high
sample-efficiency. However, in real-world scenarios, where the trajectory
distribution of most of the tasks dynamically shifts, model fitting on
continuously aggregated data alone would be futile. 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, which involves assigning
importance weights to each past demonstration. We show experiments where the
robot is trained from a diversity of environmental tasks and is also able to
adapt to an unseen environment, using few-shot learning. We also developed a
prototype robot system to test our approach on the task of visual navigation,
and experimental results obtained were able to confirm these suppositions.
Related papers
- Meta-Learning with Heterogeneous Tasks [42.695853959923625]
Heterogeneous Tasks Robust Meta-learning (HeTRoM)
An efficient iterative optimization algorithm based on bi-level optimization.
Results demonstrate that our method provides flexibility, enabling users to adapt to diverse task settings.
arXiv Detail & Related papers (2024-10-24T16:32:23Z) - Towards Task Sampler Learning for Meta-Learning [37.02030832662183]
Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks.
It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models.
This paper challenges this view through empirical and theoretical analysis.
arXiv Detail & Related papers (2023-07-18T01:53:18Z) - Leveraging sparse and shared feature activations for disentangled
representation learning [112.22699167017471]
We propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation.
We validate our approach on six real world distribution shift benchmarks, and different data modalities.
arXiv Detail & Related papers (2023-04-17T01:33:24Z) - Inferring Versatile Behavior from Demonstrations by Matching Geometric
Descriptors [72.62423312645953]
Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps.
Current Imitation Learning algorithms often only consider unimodal expert demonstrations and act in a state-action-based setting.
Instead, we combine a mixture of movement primitives with a distribution matching objective to learn versatile behaviors that match the expert's behavior and versatility.
arXiv Detail & Related papers (2022-10-17T16:42:59Z) - The Effect of Diversity in Meta-Learning [79.56118674435844]
Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples.
Recent studies show that task distribution plays a vital role in the model's performance.
We study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms.
arXiv Detail & Related papers (2022-01-27T19:39:07Z) - Uni-Perceiver: Pre-training Unified Architecture for Generic Perception
for Zero-shot and Few-shot Tasks [73.63892022944198]
We present a generic perception architecture named Uni-Perceiver.
It processes a variety of modalities and tasks with unified modeling and shared parameters.
Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks.
arXiv Detail & Related papers (2021-12-02T18:59:50Z) - 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) - Combat Data Shift in Few-shot Learning with Knowledge Graph [42.59886121530736]
In real-world applications, few-shot learning paradigm often suffers from data shift.
Most existing few-shot learning approaches are not designed with the consideration of data shift.
We propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations.
arXiv Detail & Related papers (2021-01-27T12:35:18Z) - Probabilistic Active Meta-Learning [15.432006404678981]
We introduce task selection based on prior experience into a meta-learning algorithm.
We provide empirical evidence that our approach improves data-efficiency when compared to strong baselines on simulated robotic experiments.
arXiv Detail & Related papers (2020-07-17T12:51:42Z) - Adaptive Task Sampling for Meta-Learning [79.61146834134459]
Key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time.
We propose an adaptive task sampling method to improve the generalization performance.
arXiv Detail & Related papers (2020-07-17T03:15:53Z)
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.