Learning from Demonstration (LfD) is a widely used technique for skill
acquisition in robotics. However, demonstrations of the same skill may exhibit
significant variances, or learning systems may attempt to acquire different
means of the same skill simultaneously, making it challenging to encode these
motions into movement primitives. To address these challenges, we propose an
LfD framework, namely the Conditional Neural Expert Processes (CNEP), that
learns to assign demonstrations from different modes to distinct expert
networks utilizing the inherent information within the latent space to match
experts with the encoded representations. CNEP does not require supervision on
which mode the trajectories belong to. Provided experiments on artificially
generated datasets demonstrate the efficacy of CNEP. Furthermore, we compare
the performance of CNEP with another LfD framework, namely Conditional Neural
Movement Primitives (CNMP), on a range of tasks, including experiments on a
real robot. The results reveal enhanced modeling performance for movement
primitives, leading to the synthesis of trajectories that more accurately
reflect those demonstrated by experts, particularly when the model inputs
include intersection points from various trajectories. Additionally, CNEP
offers improved interpretability and faster convergence by promoting expert
specialization. Furthermore, we show that the CNEP model accomplishes obstacle
avoidance tasks with a real manipulator when provided with novel start and
destination points, in contrast to the CNMP model, which leads to collisions
with the obstacle.

通过提出一种名为条件神经专家过程（CNEP）的 LfD 框架，利用潜在空间中的内在信息将不同模式的演示分配给不同的专家网络，无需监督以确定轨迹所属的模式，该框架提供了改进的建模性能、轨迹综合能力和障碍物避免任务的能力。

条件神经专家过程用于从示范中学习

Conditional Neural Expert Processes for Learning from Demonstration

Simulating dynamic physical interactions is a critical challenge across
multiple scientific domains, with applications ranging from robotics to
material science. For mesh-based simulations, Graph Network Simulators (GNSs)
pose an efficient alternative to traditional physics-based simulators. Their
inherent differentiability and speed make them particularly well-suited for
inverse design problems. Yet, adapting to new tasks from limited available data
is an important aspect for real-world applications that current methods
struggle with. We frame mesh-based simulation as a meta-learning problem and
use a recent Bayesian meta-learning method to improve GNSs adaptability to new
scenarios by leveraging context data and handling uncertainties. Our approach,
latent task-specific graph network simulator, uses non-amortized task posterior
approximations to sample latent descriptions of unknown system properties.
Additionally, we leverage movement primitives for efficient full trajectory
prediction, effectively addressing the issue of accumulating errors encountered
by previous auto-regressive methods. We validate the effectiveness of our
approach through various experiments, performing on par with or better than
established baseline methods. Movement primitives further allow us to
accommodate various types of context data, as demonstrated through the
utilization of point clouds during inference. By combining GNSs with
meta-learning, we bring them closer to real-world applicability, particularly
in scenarios with smaller datasets.

利用最近的贝叶斯元学习方法将网格模拟作为元学习问题来处理，通过利用上下文数据和处理不确定性来提高图网络模拟器对新场景的适应性，结合运动原语实现高效的全轨迹预测，并验证了该方法的有效性。

潜在的任务特定图网络模拟器

Latent Task-Specific Graph Network Simulators

Movement primitives are trainable parametric models that reproduce robotic
movements starting from a limited set of demonstrations. Previous works
proposed simple linear models that exhibited high sample efficiency and
generalization power by allowing temporal modulation of movements (reproducing
movements faster or slower), blending (merging two movements into one),
via-point conditioning (constraining a movement to meet some particular
via-points) and context conditioning (generation of movements based on an
observed variable, e.g., position of an object). Previous works have proposed
neural network-based motor primitive models, having demonstrated their capacity
to perform tasks with some forms of input conditioning or time-modulation
representations. However, there has not been a single unified deep motor
primitive's model proposed that is capable of all previous operations, limiting
neural motor primitive's potential applications. This paper proposes a deep
movement primitive architecture that encodes all the operations above and uses
a Bayesian context aggregator that allows a more sound context conditioning and
blending. Our results demonstrate our approach can scale to reproduce complex
motions on a larger variety of input choices compared to baselines while
maintaining operations of linear movement primitives provide.

本文提出了一个深度运动原语体系结构，将所有以前的操作编码，并使用贝叶斯上下文聚合器，同时保持了线性运动基元的操作，表明该方法在较大的输入选择变化时能更好地扩展以复制复杂动作。