Presented by Yuqian Jiang 2/27/2019 Back to the BlocksWorld: Learning New Actions through Situated Human-Robot Dialogue Presented by Yuqian Jiang 2/27/2019

Source: https://goo.gl/images/nS1JgX PROBLEM Learn new actions through situated human-robot dialogue ...in a simplified blocks world Source: https://goo.gl/images/nS1JgX

PROBLEM How does a robot learn the action stack from a dialogue if it knows primitive actions: open gripper, close gripper, move

MOTIVATION When robots work side-by-side with humans, they can learn new tasks from their human partners through dialogue Challenges: Human language: discrete and symbolic, robot representation: continuous How to represent new knowledge so it can generalize? How should the human teach new actions?

RELATED WORK Following natural language instructions Kollar et al., 2010; Tellex et al., 2011; Chen et al., 2010 Learning by demonstration Cakmak et al., 2010 Connecting language with lower level control systems Kress-Gazit et al., 2008; Siskind, 1999; Matuszek et al., 2012 Using dialogue for action learning Cantrell et al., 2012; Mohan et al., 2013

METHOD A dialogue system for action learning

Intent Recognizer: Command or confirmation Semantic Processor: Implemented using Combinatory Categorial Grammar (CCG) Extracts action and object properties

on the red block on your right.” “stack the blue block on the red block on your right.”

Perception Modules: From camera image and internal status A conjunction of predicates representing environment Reference Solver: Grounds objects in the semantic representation to the objects in the robot’s perception

on the red block on your right.” “stack the blue block on the red block on your right.”

Dialogue manager: A dialogue policy decides the dialogue acts based on the current state Language Generator: Pre-defined templates

ACTION MODULES Action knowledge Action execution Action learning

ACTION LEARNING If an action is not in the knowledge base, ask for instructions Follow the instructions Extract a goal state describing the action effects

ACTION LEARNING

EXPERIMENTS Teach five new actions under two strategies Pickup, Grab, Drop, ClearTop, Stack step-by-step instructions vs. one-shot instructions (“pick up the blue block and put it on top of the red block”) Five participants (more will be recruited)

EXPERIMENTS

RESULTS: Teaching Completion All failed teaching dialogues are one-shot instructions.

RESULTS: Teaching Duration Step-by-step dialogues take longer to learn.

Step-by-step instructions have better generalization. RESULTS: Execution Step-by-step instructions have better generalization.

CONCLUSION An approach to learn new actions from human-robot dialogue On top of a layered planning/execution system Integrated with language and perception modules Success in generalizing to new situations in blocks world

CRITIQUE Simplified domain with only 3 low-level actions Cannot learn high-level actions that cannot be sequenced using these low-level actions Cannot learn actions that involve objects that cannot be grounded Is it really learning a new action, or just a new word that describes a goal using existing actions?

CRITIQUE Only learns action effects, but no preconditions Experiments do test situations that violate preconditions, such as picking up a block that has another block on top Again, only successful because the preconditions of the underlying actions are modeled

CRITIQUE Evaluation Nothing surprising about the collaborative/non-collaborative results Prefer to see more details on other modules of the system, and evaluation of their robustness

CRITIQUE Challenges: ✔ Human language: discrete and symbolic, robot representation: continuous ? How to represent new knowledge so it can generalize? ? How should the human teach new actions?