8 research outputs found
Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees
In this paper, we show how Behavior Trees that have performance guarantees,
in terms of safety and goal convergence, can be extended with components that
were designed using machine learning, without destroying those performance
guarantees.
Machine learning approaches such as reinforcement learning or learning from
demonstration can be very appealing to AI designers that want efficient and
realistic behaviors in their agents. However, those algorithms seldom provide
guarantees for solving the given task in all different situations while keeping
the agent safe. Instead, such guarantees are often easier to find for manually
designed model based approaches. In this paper we exploit the modularity of
Behavior trees to extend a given design with an efficient, but possibly
unreliable, machine learning component in a way that preserves the guarantees.
The approach is illustrated with an inverted pendulum example.Comment: Submitted to IEEE Transactions on Game
An Extended Convergence Result for Behaviour Tree Controllers
Behavior trees (BTs) are an optimally modular framework to assemble
hierarchical hybrid control policies from a set of low-level control policies
using a tree structure. Many robotic tasks are naturally decomposed into a
hierarchy of control tasks, and modularity is a well-known tool for handling
complexity, therefor behavior trees have garnered widespread usage in the
robotics community. In this paper, we study the convergence of BTs, in the
sense of reaching a desired part of the state space. Earlier results on BT
convergence were often tailored to specific families of BTs, created using
different design principles. The results of this paper generalize the earlier
results and also include new cases of cyclic switching not covered in the
literature.Comment: Submitted to the IEEE Transactions on Robotics (T-RO
esa/pykep: Upgrade to pygmo 2.0 and more
Updating to pygmo 2 (and dropping PyGMO)
Adding the Pontryagin module
Bug fixes on planet ephs (affecting low inclinations and eccentricites)
Implementation of modified equinoctial parameters
Adding more examples
Documentation fixes
Clang format and pep8 enforced
Some API improvements (more kwargs)
name changed to pykep (not PyKEP
A System for Autonomous Seaweed Farm Inspection with an Underwater Robot
This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup