Pouring is one of the most commonly executed tasks in humans' daily lives, whose accuracy is affected by multiple factors, including the type of material to be poured and the geometry of the source and receiving containers. In this work, we propose a self-supervised learning approach that learns the pouring dynamics, pouring motion, and outcomes from unsupervised demonstrations for accurate pouring. The learned pouring model is then generalized by self-supervised practicing to different conditions such as using unaccustomed pouring cups. We have evaluated the proposed approach first with one container from the training set and four new but similar containers. The proposed approach achieved better pouring accuracy than a regular human with a similar pouring speed for all five cups. Both the accuracy and pouring speed outperform state-of-the-art works. We have also evaluated the proposed self-supervised generalization approach using unaccustomed containers that are far different from the ones in the training set. The self-supervised generalization reduces the pouring error of the unaccustomed containers to the desired accuracy level.
Robotic Skill Diversification via Active Mutation of Reward Functions in Reinforcement Learning During a Liquid Pouring Task
Cognitive Robotics, Delft University of Technology（德鲁特理工大学认知机器人学）；Department of Mechanical Engineering, Politecnico di Milano（米兰理工学院机械工程系）；Istituto Dalle Molle di studi sull’intelligenza artificiale (IDSIA USI-SUPSI), Scuola universitaria professionale della Svizzera italiana, DTI（智能人工智能研究所（IDSIA USI-SUPSI），瑞士意大利大学专业学院，DTI）
Imperial College London（伦敦帝国理工学院）；Tencent Robotics X（腾讯 Robotics X）；Bielefeld University（比勒费尔德大学）
To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. Traditional deep imitation learning techniques for implementing autonomous robotic pouring have an inherent black-box effect and require a large amount of demonstration data for model training. To address these issues, an Explainable Hierarchical Imitation Learning (EHIL) method is proposed in this paper such that a robot can learn high-level general knowledge and execute low-level actions across multiple drink pouring scenarios. Moreov…
Robots can generalize manipulation skills between different scenarios by adapting to the features of the objects being manipulated. Selecting the set of relevant features for generalizing skills has usually been performed manually by a human. Alternatively, a robot can learn to select relevant features autonomously. However, feature selection usually requires a large amount of training data, which would require many demonstrations. In order to learn the relevant features more efficiently, we propose using a meta-level prior to transfer the relevance of features from previously learned skills. …
In contrast to humans and animals who naturally execute seamless motions, learning and smoothly executing sequences of actions remains a challenge in robotics. This paper introduces a novel skill-agnostic framework that learns to sequence and blend skills based on differentiable optimization. Our approach encodes sequences of previously-defined skills as quadratic programs (QP), whose parameters determine the relative importance of skills along the task. Seamless skill sequences are then learned from demonstrations by exploiting differentiable optimization layers and a tailored loss formulated…
Bogazici University（海峡大学）；Ozyegin University（厄兹耶因大学）；University of Luebeck（吕贝克大学）
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). In this paper, we propose a novel LfD+RL framework, namely Adaptive Conditional Neural Movement Primitives (ACNMP), that allows efficient policy improvement in novel environments and effective skill transfer between different agents. This is achieved through exploiting the latent representation learned by the underlying Conditional Neural Process (CNP) model, and simultaneous…
The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible generative planning that can combine primitive abilities in novel combinations and thus generalize across a wide variety of problems. In order to plan with primitive actions, we must have models of the actions: under what circumstances will executing this primitive successfully achieve some particular effect in the world? We use, and develop novel improvements on, state-of-the-art methods for active learni…
ReTrace: Cross-Stage Failure Attribution for Self-Improving Robotic Manipulation
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机与人工智能学院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；Singapore Management University（新加坡管理大学）
FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation
Southern University of Science and Technology（南方科技大学）；National University of Singapore（新加坡国立大学）；Smart Systems Institute, National University of Singapore（新加坡国立大学智能系统研究院）
