Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks

• URL: http://arxiv.org/abs/2211.06134v2
• Date: Tue, 18 Apr 2023 07:34:55 GMT
• Title: Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks
• Authors: Kuan Fang, Toki Migimatsu, Ajay Mandlekar, Li Fei-Fei, Jeannette Bohg
• Abstract summary: We introduce Active Task Randomization (ATR), an approach that learns robust skills through the unsupervised generation of training tasks. ATR selects suitable tasks, which consist of an initial environment state and manipulation goal, for learning robust skills by balancing the diversity and feasibility of the tasks. We demonstrate that the learned skills can be composed by a task planner to solve unseen sequential manipulation problems based on visual inputs.
• Score: 37.73239471412444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Solving real-world manipulation tasks requires robots to have a repertoire of skills applicable to a wide range of circumstances. When using learning-based methods to acquire such skills, the key challenge is to obtain training data that covers diverse and feasible variations of the task, which often requires non-trivial manual labor and domain knowledge. In this work, we introduce Active Task Randomization (ATR), an approach that learns robust skills through the unsupervised generation of training tasks. ATR selects suitable tasks, which consist of an initial environment state and manipulation goal, for learning robust skills by balancing the diversity and feasibility of the tasks. We propose to predict task diversity and feasibility by jointly learning a compact task representation. The selected tasks are then procedurally generated in simulation using graph-based parameterization. The active selection of these training tasks enables skill policies trained with our framework to robustly handle a diverse range of objects and arrangements at test time. We demonstrate that the learned skills can be composed by a task planner to solve unseen sequential manipulation problems based on visual inputs. Compared to baseline methods, ATR can achieve superior success rates in single-step and sequential manipulation tasks.

Related papers

• Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks [101.40633115037983]
  Instruction tuning (IT) achieves impressive zero-shot generalization results by training large language models (LLMs) on a massive amount of diverse tasks with instructions. How to select new tasks to improve the performance and generalizability of IT models remains an open question. We propose active instruction tuning based on prompt uncertainty, a novel framework to identify informative tasks, and then actively tune the models on the selected tasks.
  arXiv  Detail & Related papers  (2023-11-01T04:40:05Z)
• Reinforcement Learning with Success Induced Task Prioritization [68.8204255655161]
  We introduce Success Induced Task Prioritization (SITP), a framework for automatic curriculum learning. The algorithm selects the order of tasks that provide the fastest learning for agents. We demonstrate that SITP matches or surpasses the results of other curriculum design methods.
  arXiv  Detail & Related papers  (2022-12-30T12:32:43Z)
• Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation [10.219833196479142]
  We address the challenge of transferring knowledge within a family of similar tasks by leveraging multiple skill priors. Our method learns a latent action space representing the skill embedding from demonstrated trajectories for each prior task. We have evaluated our method on a set of peg-in-hole insertion tasks and demonstrate better generalization to new tasks that have never been encountered during training.
  arXiv  Detail & Related papers  (2022-09-19T10:31:13Z)
• An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems [4.675744559395732]
  Multitask learning assumes that models capable of learning from multiple tasks can achieve better quality and efficiency via knowledge transfer. State of the art ML models rely on high customization for each task and leverage size and data scale rather than scaling the number of tasks. We propose an evolutionary method that can generate a large scale multitask model and can support the dynamic and continuous addition of new tasks.
  arXiv  Detail & Related papers  (2022-05-25T13:10:47Z)
• Variational Multi-Task Learning with Gumbel-Softmax Priors [105.22406384964144]
  Multi-task learning aims to explore task relatedness to improve individual tasks. We propose variational multi-task learning (VMTL), a general probabilistic inference framework for learning multiple related tasks.
  arXiv  Detail & Related papers  (2021-11-09T18:49:45Z)
• Efficiently Identifying Task Groupings for Multi-Task Learning [55.80489920205404]
  Multi-task learning can leverage information learned by one task to benefit the training of other tasks. We suggest an approach to select which tasks should train together in multi-task learning models. Our method determines task groupings in a single training run by co-training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss.
  arXiv  Detail & Related papers  (2021-09-10T02:01:43Z)
• Discovering Generalizable Skills via Automated Generation of Diverse Tasks [82.16392072211337]
  We propose a method to discover generalizable skills via automated generation of a diverse set of tasks. As opposed to prior work on unsupervised discovery of skills, our method pairs each skill with a unique task produced by a trainable task generator. A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective. The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks.
  arXiv  Detail & Related papers  (2021-06-26T03:41:51Z)
• Adaptive Procedural Task Generation for Hard-Exploration Problems [78.20918366839399]
  We introduce Adaptive Procedural Task Generation (APT-Gen) to facilitate reinforcement learning in hard-exploration problems. At the heart of our approach is a task generator that learns to create tasks from a parameterized task space via a black-box procedural generation module. To enable curriculum learning in the absence of a direct indicator of learning progress, we propose to train the task generator by balancing the agent's performance in the generated tasks and the similarity to the target tasks.
  arXiv  Detail & Related papers  (2020-07-01T09:38:51Z)

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.