The ongoing deep learning revolution has allowed computers to outclass humans
in various games and perceive features imperceptible to humans during
classification tasks. Current machine learning techniques have clearly
distinguished themselves in specialized tasks. However, we have yet to see
robots capable of performing multiple tasks at an expert level. Most work in
this field is focused on the development of more sophisticated learning
algorithms for a robot's controller given a largely static and presupposed
robotic design. By focusing on the development of robotic bodies, rather than
neural controllers, I have discovered that robots can be designed such that
they overcome many of the current pitfalls encountered by neural controllers in
multitask settings. Through this discovery, I also present novel metrics to
explicitly measure the learning ability of a robotic design and its resistance
to common problems such as catastrophic interference.
Traditionally, the physical robot design requires human engineers to plan
every aspect of the system, which is expensive and often relies on human
intuition. In contrast, within the field of evolutionary robotics, evolutionary
algorithms are used to automatically create optimized designs, however, such
designs are often still limited in their ability to perform in a multitask
setting. The metrics created and presented here give a novel path to automated
design that allow evolved robots to synergize with their controller to improve
the computational efficiency of their learning while overcoming catastrophic
interference.
Overall, this dissertation intimates the ability to automatically design
robots that are more general purpose than current robots and that can perform
various tasks while requiring less computation.

通过设计机器人体形，可以在多任务场景下克服神经控制器所遇到的许多问题，从而实现设计更通用、需要更少计算力的机器人。

探究机器人设计对学习和神经控制的影响

Exploring the effects of robotic design on learning and neural control

Over the last decade, the use of robots in production and daily life has
increased. With increasingly complex tasks and interaction in different
environments including humans, robots are required a higher level of autonomy
for efficient deliberation. Task planning is a key element of deliberation. It
combines elementary operations into a structured plan to satisfy a prescribed
goal, given specifications on the robot and the environment. In this
manuscript, we present a survey on recent advances in the application of logic
programming to the problem of task planning. Logic programming offers several
advantages compared to other approaches, including greater expressivity and
interpretability which may aid in the development of safe and reliable robots.
We analyze different planners and their suitability for specific robotic
applications, based on expressivity in domain representation, computational
efficiency and software implementation. In this way, we support the robotic
designer in choosing the best tool for his application.

这篇文章研究了逻辑编程在机器人任务规划中的应用，探讨了不同规划器的适用性，为机器人设计师选择最佳工具提供支持。

逻辑编程用于机器人任务决策规划

Logic programming for deliberative robotic task planning

Organisms result from adaptive processes interacting across different time
scales. One such interaction is that between development and evolution. Models
have shown that development sweeps over several traits in a single agent,
sometimes exposing promising static traits. Subsequent evolution can then
canalize these rare traits. Thus, development can, under the right conditions,
increase evolvability. Here, we report on a previously unknown phenomenon when
embodied agents are allowed to develop and evolve: Evolution discovers body
plans robust to control changes, these body plans become genetically
assimilated, yet controllers for these agents are not assimilated. This allows
evolution to continue climbing fitness gradients by tinkering with the
developmental programs for controllers within these permissive body plans. This
exposes a previously unknown detail about the Baldwin effect: instead of all
useful traits becoming genetically assimilated, only traits that render the
agent robust to changes in other traits become assimilated. We refer to this as
differential canalization. This finding also has implications for the
evolutionary design of artificial and embodied agents such as robots: robots
robust to internal changes in their controllers may also be robust to external
changes in their environment, such as transferal from simulation to reality or
deployment in novel environments.

该研究报道了一种之前未知的现象，即进化发现了对控制变化具有鲁棒性的身体计划，这些身体计划成为遗传上同化的，但这些代理人的控制器并未被同化。这发现对于人工和实体代理人的进化设计有影响，如机器人对其控制器内部变化具有鲁棒性，也可能对其环境的外部变化具有鲁棒性。