Diffused Task-Agnostic Milestone Planner
- URL: http://arxiv.org/abs/2312.03395v1
- Date: Wed, 6 Dec 2023 10:09:22 GMT
- Title: Diffused Task-Agnostic Milestone Planner
- Authors: Mineui Hong, Minjae Kang, Songhwai Oh
- Abstract summary: We propose a method to utilize a diffusion-based generative sequence model to plan a series of milestones in a latent space.
The proposed method can learn control-relevant, low-dimensional latent representations of milestones, which makes it possible to efficiently perform long-term planning and vision-based control.
- Score: 13.042155799536657
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Addressing decision-making problems using sequence modeling to predict future
trajectories shows promising results in recent years. In this paper, we take a
step further to leverage the sequence predictive method in wider areas such as
long-term planning, vision-based control, and multi-task decision-making. To
this end, we propose a method to utilize a diffusion-based generative sequence
model to plan a series of milestones in a latent space and to have an agent to
follow the milestones to accomplish a given task. The proposed method can learn
control-relevant, low-dimensional latent representations of milestones, which
makes it possible to efficiently perform long-term planning and vision-based
control. Furthermore, our approach exploits generation flexibility of the
diffusion model, which makes it possible to plan diverse trajectories for
multi-task decision-making. We demonstrate the proposed method across offline
reinforcement learning (RL) benchmarks and an visual manipulation environment.
The results show that our approach outperforms offline RL methods in solving
long-horizon, sparse-reward tasks and multi-task problems, while also achieving
the state-of-the-art performance on the most challenging vision-based
manipulation benchmark.
Related papers
- MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [52.739500459903724]
Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation.
We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents.
We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
arXiv Detail & Related papers (2024-11-26T17:53:44Z) - Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile Diffusion Planner [12.360598915420255]
We propose textbfSODP, a two-stage framework that learns a textbfDiffusion textbfPlanner, which is generalizable for various downstream tasks.
In the pre-training stage, we train a foundation diffusion planner that extracts general planning capabilities by modeling the versatile distribution of multi-task trajectories.
Then for downstream tasks, we adopt RL-based fine-tuning with task-specific rewards to fast refine the diffusion planner.
arXiv Detail & Related papers (2024-09-30T05:05:37Z) - Adaptive Planning with Generative Models under Uncertainty [20.922248169620783]
Planning with generative models has emerged as an effective decision-making paradigm across a wide range of domains.
While continuous replanning at each timestep might seem intuitive because it allows decisions to be made based on the most recent environmental observations, it results in substantial computational challenges.
Our work addresses this challenge by introducing a simple adaptive planning policy that leverages the generative model's ability to predict long-horizon state trajectories.
arXiv Detail & Related papers (2024-08-02T18:07:53Z) - CUDC: A Curiosity-Driven Unsupervised Data Collection Method with
Adaptive Temporal Distances for Offline Reinforcement Learning [62.58375643251612]
We propose a Curiosity-driven Unsupervised Data Collection (CUDC) method to expand feature space using adaptive temporal distances for task-agnostic data collection.
With this adaptive reachability mechanism in place, the feature representation can be diversified, and the agent can navigate itself to collect higher-quality data with curiosity.
Empirically, CUDC surpasses existing unsupervised methods in efficiency and learning performance in various downstream offline RL tasks of the DeepMind control suite.
arXiv Detail & Related papers (2023-12-19T14:26:23Z) - Planning as In-Painting: A Diffusion-Based Embodied Task Planning
Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems.
We propose a task-agnostic method named 'planning as in-painting'
The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z) - Efficient Planning with Latent Diffusion [18.678459478837976]
Temporal abstraction and efficient planning pose significant challenges in offline reinforcement learning.
Latent action spaces offer a more flexible paradigm, capturing only possible actions within the behavior policy support.
This paper presents a unified framework for continuous latent action space representation learning and planning by leveraging latent, score-based diffusion models.
arXiv Detail & Related papers (2023-09-30T08:50:49Z) - Compositional Foundation Models for Hierarchical Planning [52.18904315515153]
We propose a foundation model which leverages expert foundation model trained on language, vision and action data individually together to solve long-horizon tasks.
We use a large language model to construct symbolic plans that are grounded in the environment through a large video diffusion model.
Generated video plans are then grounded to visual-motor control, through an inverse dynamics model that infers actions from generated videos.
arXiv Detail & Related papers (2023-09-15T17:44:05Z) - Planning with Diffusion for Flexible Behavior Synthesis [125.24438991142573]
We consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem.
The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories.
arXiv Detail & Related papers (2022-05-20T07:02:03Z) - Model-Based Reinforcement Learning via Latent-Space Collocation [110.04005442935828]
We argue that it is easier to solve long-horizon tasks by planning sequences of states rather than just actions.
We adapt the idea of collocation, which has shown good results on long-horizon tasks in optimal control literature, to the image-based setting by utilizing learned latent state space models.
arXiv Detail & Related papers (2021-06-24T17:59:18Z) - PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals [14.315501760755609]
PlanGAN is a model-based algorithm for solving multi-goal tasks in environments with sparse rewards.
Our studies indicate that PlanGAN can achieve comparable performance whilst being around 4-8 times more sample efficient.
arXiv Detail & Related papers (2020-06-01T12:53:09Z)
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.