Recent advances in diffusion models have demonstrated their strong
capabilities in generating high-fidelity samples from complex distributions
through an iterative refinement process. Despite the empirical success of
diffusion models in motion planning and control, the model-free nature of these
methods does not leverage readily available model information and limits their
generalization to new scenarios beyond the training data (e.g., new robots with
different dynamics). In this work, we introduce Model-Based Diffusion (MBD), an
optimization approach using the diffusion process to solve trajectory
optimization (TO) problems without data. The key idea is to explicitly compute
the score function by leveraging the model information in TO problems, which is
why we refer to our approach as model-based diffusion. Moreover, although MBD
does not require external data, it can be naturally integrated with data of
diverse qualities to steer the diffusion process. We also reveal that MBD has
interesting connections to sampling-based optimization. Empirical evaluations
show that MBD outperforms state-of-the-art reinforcement learning and
sampling-based TO methods in challenging contact-rich tasks. Additionally,
MBD's ability to integrate with data enhances its versatility and practical
applicability, even with imperfect and infeasible data (e.g., partial-state
demonstrations for high-dimensional humanoids), beyond the scope of standard
diffusion models.

无需数据的情况下通过扩散过程解决轨迹优化问题的模型驱动扩散（MBD）方法优于强化学习和基于采样的轨迹优化方法，且能与数据集集成。

基于模型的扩散轨迹优化

Model-Based Diffusion for Trajectory Optimization

Imitation learning from human demonstrations can teach robots complex
manipulation skills, but is time-consuming and labor intensive. In contrast,
Task and Motion Planning (TAMP) systems are automated and excel at solving
long-horizon tasks, but they are difficult to apply to contact-rich tasks. In
this paper, we present Human-in-the-Loop Task and Motion Planning (HITL-TAMP),
a novel system that leverages the benefits of both approaches. The system
employs a TAMP-gated control mechanism, which selectively gives and takes
control to and from a human teleoperator. This enables the human teleoperator
to manage a fleet of robots, maximizing data collection efficiency. The
collected human data is then combined with an imitation learning framework to
train a TAMP-gated policy, leading to superior performance compared to training
on full task demonstrations. We compared HITL-TAMP to a conventional
teleoperation system -- users gathered more than 3x the number of demos given
the same time budget. Furthermore, proficient agents (75\%+ success) could be
trained from just 10 minutes of non-expert teleoperation data. Finally, we
collected 2.1K demos with HITL-TAMP across 12 contact-rich, long-horizon tasks
and show that the system often produces near-perfect agents. Videos and
additional results at this https URL .

这篇论文介绍了一种新颖的人机交互任务与动作规划系统 (HITL-TAMP)，它通过结合模仿学习和自动规划的优势，实现了由人类远程操作员管理机器人团队的功能，提高了数据收集效率，并在接触密集任务上训练出了出色的性能。

人机协同任务和运动规划的仿真学习

Human-in-the-Loop Task and Motion Planning for Imitation Learning

In this paper, we explore generalizable, perception-to-action robotic
manipulation for precise, contact-rich tasks. In particular, we contribute a
framework for closed-loop robotic manipulation that automatically handles a
category of objects, despite potentially unseen object instances and
significant intra-category variations in shape, size and appearance. Previous
approaches typically build a feedback loop on top of a real-time 6-DOF pose
estimator. However, representing an object with a parameterized transformation
from a fixed geometric template does not capture large intra-category shape
variation. Hence we adopt the keypoint-based object representation proposed in
kPAM for category-level pick-and-place, and extend it to closed-loop
manipulation policies with contact-rich tasks. We first augment keypoints with
local orientation information. Using the oriented keypoints, we propose a novel
object-centric action representation in terms of regulating the linear/angular
velocity or force/torque of these oriented keypoints. This formulation is
surprisingly versatile -- we demonstrate that it can accomplish contact-rich
manipulation tasks that require precision and dexterity for a category of
objects with different shapes, sizes and appearances, such as peg-hole
insertion for pegs and holes with significant shape variation and tight
clearance. With the proposed object and action representation, our framework is
also agnostic to the robot grasp pose and initial object configuration, making
it flexible for integration and deployment.

本文中，我们探讨了可推广的感知行动机器人操纵，解决了一类物体操作的自动化，实现了接触丰富的操作任务，提出了一种基于关键点的物体表示方法，并扩展为闭环操作策略。我们提出了一种新颖的以物体为中心的动作表示方法，它可以完成需要精度和灵巧性的接触丰富操纵任务。