Intelligent robots need to generate and execute plans. In order to deal with
the complexity of real environments, planning makes some assumptions about the
world. When executing plans, the assumptions are usually not met. Most works
have focused on the negative impact of this fact and the use of replanning
after execution failures. Instead, we focus on the positive impact, or
opportunities to find better plans. When planning, the proposed technique finds
and stores those opportunities. Later, during execution, the monitoring system
can use them to focus perception and repair the plan, instead of replanning
from scratch. Experiments in several paradigmatic robotic tasks show how the
approach outperforms standard replanning strategies.

智能机器人在规划和执行计划过程中，通过监测系统寻找机会，以解决复杂环境中执行失败的问题，并超过常规的重新规划策略。

智能执行计划分析系统

Intelligent Execution through Plan Analysis

Following natural language instructions by executing actions in digital
environments (e.g. web-browsers and REST APIs) is a challenging task for
language model (LM) agents. Unfortunately, LM agents often fail to generalize
to new environments without human demonstrations. This work presents BAGEL, a
method for bootstrapping LM agents without human supervision. BAGEL converts a
seed set of randomly explored trajectories or synthetic instructions, into
demonstrations, via round-trips between two noisy LM components: an LM labeler
which converts a trajectory into a synthetic instruction, and a zero-shot LM
agent which maps the synthetic instruction into a refined trajectory. By
performing these round-trips iteratively, BAGEL quickly converts the initial
distribution of trajectories towards those that are well-described by natural
language. We use BAGEL demonstrations to adapt a zero shot LM agent at test
time via in-context learning over retrieved demonstrations, and find
improvements of over 2-13% absolute on ToolQA and MiniWob++, with up to 13x
reduction in execution failures.

使用 BAGEL 方法在没有人类监督的情况下通过往返于两个噪音的语言模型组件：将轨迹转换为合成指令的 LM 标签器和将合成指令映射为精细轨迹的零射语言模型代理，将随机探索的轨迹或合成指令转化为演示，快速将初始轨迹分布转换为自然语言描述的轨迹，在测试时使用 BAGEL 演示通过检索到的演示进行上下文学习以对零射语言模型代理进行调整，在 ToolQA 和 MiniWob++ 上获得 2-13% 绝对改进，执行失败率减少了最多 13 倍。

通过语言指导探索引导引导启动代理

BAGEL: Bootstrapping Agents by Guiding Exploration with Language

Dynamic Movement Primitives have successfully been used to realize imitation
learning, trial-and-error learning, reinforce- ment learning, movement
recognition and segmentation and control. Because of this they have become a
popular represen- tation for motor primitives. In this work, we showcase how
DMPs can be reformulated as a probabilistic linear dynamical system with
control inputs. Through this probabilistic repre- sentation of DMPs, algorithms
such as Kalman filtering and smoothing are directly applicable to perform
inference on pro- prioceptive sensor measurements during execution. We show
that inference in this probabilistic model automatically leads to a feedback
term to online modulate the execution of a DMP. Furthermore, we show how
inference allows us to measure the likelihood that we are successfully
executing a given motion primitive. In this context, we show initial results of
using the probabilistic model to detect execution failures on a simulated
movement primitive dataset.

本文介绍了如何将动态运动原始 (Dynamic Movement Primitives) 重构为具有控制输入的概率线性动态系统。通过这种概率表示，本文显示了卡尔曼滤波和平滑等算法在执行期间对前感觉传感器测量进行推理的可直接应用。我们进一步展示了推理如何通过反馈项自动调节 DMP 的执行，并测量成功执行给定运动基元的可能性。在这种情况下，我们展示了使用概率模型在模拟运动基元数据集中检测执行失败的初步结果。