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Papers by thao hien nguyen
Autonomous Robots
We often specify tasks for a robot using temporal language that can include different levels of a... more We often specify tasks for a robot using temporal language that can include different levels of abstraction. For example, the command "go to the kitchen before going to the second floor" contains spatial abstraction, given that "floor" consists of individual rooms that can also be referred to in isolation ("kitchen", for example). There is also a temporal ordering of events, defined by the word "before". Previous works have used syntactically co-safe Linear Temporal Logic (sc-LTL) to interpret temporal language (such as "before"), and Abstract Markov Decision Processes (AMDPs) to interpret hierarchical abstractions (such as "kitchen" and "second floor"), separately. To handle both types of commands at once, we introduce the Abstract Product Markov Decision Process (AP-MDP), a novel approach capable of representing non-Markovian reward functions at different levels of abstractions. The AP-MDP framework translates LTL into its corresponding automata, creates a product Markov Decision Process (MDP) of the LTL specification and the environment MDP, and decomposes the problem into subproblems to enable efficient planning with abstractions. AP-MDP performs faster than a non-hierarchical method of solving LTL problems in over 95% of path planning tasks, and this number only increases as the size of the environment domain increases. In a cleanup world domain, AP-MDP performs faster in over 98% of tasks. We also present a neural sequence-tosequence model trained to translate language commands into LTL expression, and a new corpus of non-Markovian language commands spanning different levels of abstraction. We test our framework with the collected language commands on two drones, demonstrating that our approach enables robots to efficiently solve temporal commands at different levels of abstraction in both indoor and outdoor environments.
Autonomous Robots, 2021
Natural language object retrieval is a highly useful yet challenging task for robots in human-cen... more Natural language object retrieval is a highly useful yet challenging task for robots in human-centric environments. Previous work has primarily focused on commands specifying the desired object’s type such as “scissors” and/or visual attributes such as “red,” thus limiting the robot to only known object classes. We develop a model to retrieve objects based on descriptions of their usage. The model takes in a language command containing a verb, for example “Hand me something to cut ,” and RGB images of candidate objects; and outputs the object that best satisfies the task specified by the verb. Our model directly predicts an object’s appearance from the object’s use specified by a verb phrase, without needing an object’s class label. Based on contextual information present in the language commands, our model can generalize to unseen object classes and unknown nouns in the commands. Our model correctly selects objects out of sets of five candidates to fulfill natural language commands, and achieves a mean reciprocal rank of 77.4% on a held-out test set of unseen ImageNet object classes and 69.1% on unseen object classes and unknown nouns. Our model also achieves a mean reciprocal rank of 71.8% on unseen YCB object classes, which have a different image distribution from ImageNet. We demonstrate our model on a KUKA LBR iiwa robot arm, enabling the robot to retrieve objects based on natural language descriptions of their usage (Video recordings of the robot demonstrations can be found at https://youtu.be/WMAdGhMmXEQ ). We also present a new dataset of 655 verb-object pairs denoting object usage over 50 verbs and 216 object classes (The dataset and code for the project can be found at https://github.com/Thaonguyen3095/affordance-language ).
Autonomous Robots
We often specify tasks for a robot using temporal language that can include different levels of a... more We often specify tasks for a robot using temporal language that can include different levels of abstraction. For example, the command "go to the kitchen before going to the second floor" contains spatial abstraction, given that "floor" consists of individual rooms that can also be referred to in isolation ("kitchen", for example). There is also a temporal ordering of events, defined by the word "before". Previous works have used syntactically co-safe Linear Temporal Logic (sc-LTL) to interpret temporal language (such as "before"), and Abstract Markov Decision Processes (AMDPs) to interpret hierarchical abstractions (such as "kitchen" and "second floor"), separately. To handle both types of commands at once, we introduce the Abstract Product Markov Decision Process (AP-MDP), a novel approach capable of representing non-Markovian reward functions at different levels of abstractions. The AP-MDP framework translates LTL into its corresponding automata, creates a product Markov Decision Process (MDP) of the LTL specification and the environment MDP, and decomposes the problem into subproblems to enable efficient planning with abstractions. AP-MDP performs faster than a non-hierarchical method of solving LTL problems in over 95% of path planning tasks, and this number only increases as the size of the environment domain increases. In a cleanup world domain, AP-MDP performs faster in over 98% of tasks. We also present a neural sequence-tosequence model trained to translate language commands into LTL expression, and a new corpus of non-Markovian language commands spanning different levels of abstraction. We test our framework with the collected language commands on two drones, demonstrating that our approach enables robots to efficiently solve temporal commands at different levels of abstraction in both indoor and outdoor environments.
Autonomous Robots, 2021
Natural language object retrieval is a highly useful yet challenging task for robots in human-cen... more Natural language object retrieval is a highly useful yet challenging task for robots in human-centric environments. Previous work has primarily focused on commands specifying the desired object’s type such as “scissors” and/or visual attributes such as “red,” thus limiting the robot to only known object classes. We develop a model to retrieve objects based on descriptions of their usage. The model takes in a language command containing a verb, for example “Hand me something to cut ,” and RGB images of candidate objects; and outputs the object that best satisfies the task specified by the verb. Our model directly predicts an object’s appearance from the object’s use specified by a verb phrase, without needing an object’s class label. Based on contextual information present in the language commands, our model can generalize to unseen object classes and unknown nouns in the commands. Our model correctly selects objects out of sets of five candidates to fulfill natural language commands, and achieves a mean reciprocal rank of 77.4% on a held-out test set of unseen ImageNet object classes and 69.1% on unseen object classes and unknown nouns. Our model also achieves a mean reciprocal rank of 71.8% on unseen YCB object classes, which have a different image distribution from ImageNet. We demonstrate our model on a KUKA LBR iiwa robot arm, enabling the robot to retrieve objects based on natural language descriptions of their usage (Video recordings of the robot demonstrations can be found at https://youtu.be/WMAdGhMmXEQ ). We also present a new dataset of 655 verb-object pairs denoting object usage over 50 verbs and 216 object classes (The dataset and code for the project can be found at https://github.com/Thaonguyen3095/affordance-language ).