Robot Motion Planning in Learned Latent Spaces

@article{Ichter2019RobotMP,
  title={Robot Motion Planning in Learned Latent Spaces},
  author={Brian Ichter and Marco Pavone},
  journal={IEEE Robotics and Automation Letters},
  year={2019},
  volume={4},
  pages={2407-2414}
}
This letter presents latent sampling-based motion planning (L-SBMP), a methodology toward computing motion plans for complex robotic systems by learning a plannable latent representation. Recent works in control of robotic systems have effectively leveraged local, low-dimensional embeddings of high-dimensional dynamics. In this letter, we combine these recent advances with techniques from sampling-based motion planning (SBMP) in order to design a methodology capable of planning for high… 

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References

SHOWING 1-10 OF 26 REFERENCES

Learning Sampling Distributions for Robot Motion Planning

TLDR
This paper proposes a methodology for nonuniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling, resulting in an order of magnitude improvement in terms of success rate and convergence to the optimal cost.

Approximate Inference-Based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models

TLDR
A fully probabilistic generative model is constructed with which a high-dimensional motion planning problem is transformed into a tractable inference problem and the motion trajectory is computed via an approximate inference algorithm based on a variant of the particle filter.

High-dimensional Motion Planning using Latent Variable Models via Approximate Inference

TLDR
A fully probabilistic generative model is constructed with which to transform a high-dimensional motion planning problem into a tractable inference problem and compute the optimal motion trajectory via an approximate inference algorithm based on a variant of the particle filter.

Universal Planning Networks

TLDR
This work finds that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images.

Deep visual foresight for planning robot motion

  • Chelsea FinnS. Levine
  • Computer Science
    2017 IEEE International Conference on Robotics and Automation (ICRA)
  • 2017
TLDR
This work develops a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data and enables a real robot to perform nonprehensile manipulation — pushing objects — and can handle novel objects not seen during training.

Motion Planning Networks

TLDR
This work presents Motion Planning Networks (MPNet), a neural network-based novel planning algorithm that encodes the given workspaces directly from a point cloud measurement and generates the end-to-end collision-free paths for the given start and goal configurations.

Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction

TLDR
The approach is to learn multimodal probability distributions over future human actions from a dataset of human-human exemplars and perform real-time robot policy construction in the resulting environment model through massively parallel sampling of human responses to candidate robot action sequences.

Fastron : A Learning-Based Configuration Space Model for Rapid Collision Detection for Gross Motion Planning in Changing Environments

Collision detection is a necessary but costly step for sampling-based motion planners, such as Rapidly-Exploring Random Trees [7]. Motion planning is typically performed in configuration space

Deep spatial autoencoders for visuomotor learning

TLDR
This work presents an approach that automates state-space construction by learning a state representation directly from camera images by using a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects.

Learning visual representations for perception-action systems

TLDR
This work argues in favor of task-specific, learn-able representations for vision as a sensory modality for systems that interact flexibly with uncontrolled environments and develops a grasp density for object detection in a novel scene.