Diffusion models have demonstrated strong potential for robotic trajectory
planning. However, generating coherent and long-horizon trajectories from
high-level instructions remains challenging, especially for complex tasks
requiring multiple sequential skills. We propose SkillDiffuser, an end-to-end
hierarchical planning framework integrating interpretable skill learning with
conditional diffusion planning to address this problem. At the higher level,
the skill abstraction module learns discrete, human-understandable skill
representations from visual observations and language instructions. These
learned skill embeddings are then used to condition the diffusion model to
generate customized latent trajectories aligned with the skills. It allows for
generating diverse state trajectories that adhere to the learnable skills. By
integrating skill learning with conditional trajectory generation,
SkillDiffuser produces coherent behavior following abstract instructions across
diverse tasks. Experiments on multi-task robotic manipulation benchmarks like
Meta-World and LOReL demonstrate state-of-the-art performance and
human-interpretable skill representations from SkillDiffuser.

SkillDiffuser 是一个集成可解释技能学习与条件扩散规划的分层规划框架，用于从高级指令生成连贯和长程轨迹，在复杂任务中尤其对需要多个连续技能的情况具有挑战性。

SkillDiffuser: 通过扩散式任务执行中的技能抽象实现可解释的分层规划

SkillDiffuser: Interpretable Hierarchical Planning via Skill  Abstractions in Diffusion-Based Task Execution

Humans can ground natural language commands to tasks at both abstract and
fine-grained levels of specificity. For instance, a human forklift operator can
be instructed to perform a high-level action, like "grab a pallet" or a
low-level action like "tilt back a little bit." While robots are also capable
of grounding language commands to tasks, previous methods implicitly assume
that all commands and tasks reside at a single, fixed level of abstraction.
Additionally, methods that do not use multiple levels of abstraction encounter
inefficient planning and execution times as they solve tasks at a single level
of abstraction with large, intractable state-action spaces closely resembling
real world complexity. In this work, by grounding commands to all the tasks or
subtasks available in a hierarchical planning framework, we arrive at a model
capable of interpreting language at multiple levels of specificity ranging from
coarse to more granular. We show that the accuracy of the grounding procedure
is improved when simultaneously inferring the degree of abstraction in language
used to communicate the task. Leveraging hierarchy also improves efficiency:
our proposed approach enables a robot to respond to a command within one second
on 90% of our tasks, while baselines take over twenty seconds on half the
tasks. Finally, we demonstrate that a real, physical robot can ground commands
at multiple levels of abstraction allowing it to efficiently plan different
subtasks within the same planning hierarchy.

本研究通过将命令与层级规划框架中所有可用的任务或子任务联系起来，实现了一种能够在多个抽象层级上解释语言的模型。该方法提高了命令的准确性和机器人的响应速度，使机器人可以高效地在不同的任务抽象层次上进行规划。