Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation

10/29/2019
by   Kuan Fang, et al.
10

The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal. We present Cascaded Variational Inference (CAVIN) Planner, a model-based method that hierarchically generates plans by sampling from latent spaces. To facilitate planning over long time horizons, our method learns latent representations that decouple the prediction of high-level effects from the generation of low-level motions through cascaded variational inference. This enables us to model dynamics at two different levels of temporal resolutions for hierarchical planning. We evaluate our approach in three multi-step robotic manipulation tasks in cluttered tabletop environments given high-dimensional observations. Empirical results demonstrate that the proposed method outperforms state-of-the-art model-based methods by strategically interacting with multiple objects.

READ FULL TEXT

page 2

page 6

page 8

page 13

research
08/22/2022

Efficient Planning in a Compact Latent Action Space

While planning-based sequence modelling methods have shown great potenti...
research
03/02/2021

Learning Robotic Manipulation Tasks through Visual Planning

Multi-step manipulation tasks in unstructured environments are extremely...
research
03/03/2021

Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap

We present a framework for visual action planning of complex manipulatio...
research
04/12/2016

Backward-Forward Search for Manipulation Planning

In this paper we address planning problems in high-dimensional hybrid co...
research
02/23/2022

Learning Multi-step Robotic Manipulation Policies from Visual Observation of Scene and Q-value Predictions of Previous Action

In this work, we focus on multi-step manipulation tasks that involve lon...
research
11/04/2021

Stein Variational Probabilistic Roadmaps

Efficient and reliable generation of global path plans are necessary for...
research
10/31/2020

Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference

Efficient modeling of relational data arising in physical, social, and i...

Please sign up or login with your details

Forgot password? Click here to reset