Large-scale robotic policies trained on data from diverse tasks and robotic
platforms hold great promise for enabling general-purpose robots; however,
reliable generalization to new environment conditions remains a major
challenge. Toward addressing this challenge, we propose a novel approach for
uncertainty-aware deployment of pre-trained language-conditioned imitation
learning agents. Specifically, we use temperature scaling to calibrate these
models and exploit the calibrated model to make uncertainty-aware decisions by
aggregating the local information of candidate actions. We implement our
approach in simulation using three such pre-trained models, and showcase its
potential to significantly enhance task completion rates. The accompanying code
is accessible at the link:
this https URL

大规模机器人策略、通用机器人、不确定性感知的部署、模仿学习代理、任务完成率。

在预训练语言条件下的不确定性感知部署

Uncertainty-Aware Deployment of Pre-trained Language-Conditioned  Imitation Learning Policies

In recent years, learning-based control in robotics has gained significant
attention due to its capability to address complex tasks in real-world
environments. With the advances in machine learning algorithms and
computational capabilities, this approach is becoming increasingly important
for solving challenging control problems in robotics by learning unknown or
partially known robot dynamics. Active exploration, in which a robot directs
itself to states that yield the highest information gain, is essential for
efficient data collection and minimizing human supervision. Similarly,
uncertainty-aware deployment has been a growing concern in robotic control, as
uncertain actions informed by the learned model can lead to unstable motions or
failure. However, active exploration and uncertainty-aware deployment have been
studied independently, and there is limited literature that seamlessly
integrates them. This paper presents a unified model-based reinforcement
learning framework that bridges these two tasks in the robotics control domain.
Our framework uses a probabilistic ensemble neural network for dynamics
learning, allowing the quantification of epistemic uncertainty via Jensen-Renyi
Divergence. The two opposing tasks of exploration and deployment are optimized
through state-of-the-art sampling-based MPC, resulting in efficient collection
of training data and successful avoidance of uncertain state-action spaces. We
conduct experiments on both autonomous vehicles and wheeled robots, showing
promising results for both exploration and deployment.

该研究提出了一个基于模型的强化学习框架，将主动探索和不确定性感知部署这两个任务结合了起来，通过 Jensen-Renyi 分歧量化确定性，最终在自主驾驶车辆和轮式机器人上进行了成功的实验。