Natural-language-facilitated human–robot cooperation refers to using natural language to facilitate interactive information sharing and task executions with a common goal constraint between robots and humans. Recently, natural-language-facilitated human–robot cooperation research has received increasing attention. Typical natural-language-facilitated human–robot cooperation scenarios include robotic daily assistance, robotic health caregiving, intelligent manufacturing, autonomous navigation, and robot social accompany. However, a thorough review, which can reveal latest methodologies of using natural language to facilitate human–robot cooperation, is missing. In this review, we comprehensively investigated natural-language-facilitated human–robot cooperation methodologies, by summarizing natural-language-facilitated human–robot cooperation research as three aspects (natural language instruction understanding, natural language-based execution plan generation, knowledge-world mapping). We also made in-depth analysis on theoretical methods, applications, and model advantages and disadvantages. Based on our paper review and perspective, future directions of natural-language-facilitated human–robot cooperation research were discussed.

The stability and trajectory control of a quadrotor carrying a suspended load with a fixed known mass has been extensively studied in recent years. However, the load mass is not always known beforehand in practical applications. This mass uncertainty brings uncertain disturbances to the quadrotor system, causing existing controllers to have a worse performance or to be collapsed. To improve the quadrotor’s stability in this situation, we investigate the impacts of the uncertain load mass on the quadrotor. By comparing the simulation results of two controllers — the proportional-derivative (PD) controller and the sliding mode controller (SMC) driven by a sliding mode disturbance of observer (SMDO), the quadrotor’s performance is verified to be worse as the uncertainty increases. The simulation results also show a controller with stronger robustness against disturbances is better for practical applications.

It is natural and efficient to use human natural language (NL) directly to instruct robot task executions without prior user knowledge of instruction patterns. Currently, NL-instructed robot execution (NLexe) is employed in various robotic scenarios, including manufacturing, daily assistance, and health caregiving. It is imperative to summarize the current NLexe systems and discuss future development trends to provide valuable insights for upcoming NLexe research. This review categorizes NLexe systems into four types based on the robot’s cognition level during task execution: NL-based execution control systems, NL-based execution training systems, NL-based interactive execution systems, and NL-based social execution systems. For each type of NLexe system, typical application scenarios with advantages, disadvantages, and open problems are introduced. Then, typical implementation methods and future research trends of NLexe systems are discussed to guide the future NLexe research.

To provide timely and appropriate assistance, robots must have the capability of proactively understanding a user’s personal needs, the so-called human intention inference. In human–human interaction, humans have a natural and implicit way to infer others’ intentions by selecting correlated context features and interpreting these features based on their life experience. However, robots do not have this capability and it is not realistic to build an explicit formula to associate human intentions with context. In this article, a novel fuzzy context-specific intention inference method is developed for human-like implicit human intention inference. With a fuzzy manner, context features are converted into discrete context statuses, which are similar to human subjective feelings. An intention-centered common sense database is developed consisting of correlated fuzzy context statuses, object affordances, and their relationship with human intentions. With this database, a Fuzzy Naïve Bayesian Network algorithm is adopted for implicit intention inference. Home scenario results validated the fuzzy context-specific intention inference methods reliability and lab scenario results validated the fuzzy context-specific intention inference methods effectiveness and robustness. This work is expected to develop intuitive and effective human–robot interaction, consequently enhancing the adoption of assistive technologies and improving the independence of the disabled and elderly in activities of daily living.