137 research outputs found
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Robots operating alongside humans in diverse, stochastic environments must be
able to accurately interpret natural language commands. These instructions
often fall into one of two categories: those that specify a goal condition or
target state, and those that specify explicit actions, or how to perform a
given task. Recent approaches have used reward functions as a semantic
representation of goal-based commands, which allows for the use of a
state-of-the-art planner to find a policy for the given task. However, these
reward functions cannot be directly used to represent action-oriented commands.
We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding
Network (DRAGGN), for task grounding and execution that handles natural
language from either category as input, and generalizes to unseen environments.
Our robot-simulation results demonstrate that a system successfully
interpreting both goal-oriented and action-oriented task specifications brings
us closer to robust natural language understanding for human-robot interaction.Comment: Accepted at the 1st Workshop on Language Grounding for Robotics at
ACL 201
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Robots operating alongside humans in diverse, stochastic environments must be
able to accurately interpret natural language commands. These instructions
often fall into one of two categories: those that specify a goal condition or
target state, and those that specify explicit actions, or how to perform a
given task. Recent approaches have used reward functions as a semantic
representation of goal-based commands, which allows for the use of a
state-of-the-art planner to find a policy for the given task. However, these
reward functions cannot be directly used to represent action-oriented commands.
We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding
Network (DRAGGN), for task grounding and execution that handles natural
language from either category as input, and generalizes to unseen environments.
Our robot-simulation results demonstrate that a system successfully
interpreting both goal-oriented and action-oriented task specifications brings
us closer to robust natural language understanding for human-robot interaction.Comment: Accepted at the 1st Workshop on Language Grounding for Robotics at
ACL 201
Specifying and Interpreting Reinforcement Learning Policies through Simulatable Machine Learning
Human-AI collaborative policy synthesis is a procedure in which (1) a human
initializes an autonomous agent's behavior, (2) Reinforcement Learning improves
the human specified behavior, and (3) the agent can explain the final optimized
policy to the user. This paradigm leverages human expertise and facilitates a
greater insight into the learned behaviors of an agent. Existing approaches to
enabling collaborative policy specification involve black box methods which are
unintelligible and are not catered towards non-expert end-users. In this paper,
we develop a novel collaborative framework to enable humans to initialize and
interpret an autonomous agent's behavior, rooted in principles of
human-centered design. Through our framework, we enable humans to specify an
initial behavior model in the form of unstructured, natural language, which we
then convert to lexical decision trees. Next, we are able to leverage these
human-specified policies, to warm-start reinforcement learning and further
allow the agent to optimize the policies through reinforcement learning.
Finally, to close the loop on human-specification, we produce explanations of
the final learned policy, in multiple modalities, to provide the user a final
depiction about the learned policy of the agent. We validate our approach by
showing that our model can produce >80% accuracy, and that human-initialized
policies are able to successfully warm-start RL. We then conduct a novel
human-subjects study quantifying the relative subjective and objective benefits
of varying XAI modalities(e.g., Tree, Language, and Program) for explaining
learned policies to end-users, in terms of usability and interpretability and
identify the circumstances that influence these measures. Our findings
emphasize the need for personalized explainable systems that can facilitate
user-centric policy explanations for a variety of end-users
Vision-Language Interpreter for Robot Task Planning
Large language models (LLMs) are accelerating the development of
language-guided robot planners. Meanwhile, symbolic planners offer the
advantage of interpretability. This paper proposes a new task that bridges
these two trends, namely, multimodal planning problem specification. The aim is
to generate a problem description (PD), a machine-readable file used by the
planners to find a plan. By generating PDs from language instruction and scene
observation, we can drive symbolic planners in a language-guided framework. We
propose a Vision-Language Interpreter (ViLaIn), a new framework that generates
PDs using state-of-the-art LLM and vision-language models. ViLaIn can refine
generated PDs via error message feedback from the symbolic planner. Our aim is
to answer the question: How accurately can ViLaIn and the symbolic planner
generate valid robot plans? To evaluate ViLaIn, we introduce a novel dataset
called the problem description generation (ProDG) dataset. The framework is
evaluated with four new evaluation metrics. Experimental results show that
ViLaIn can generate syntactically correct problems with more than 99% accuracy
and valid plans with more than 58% accuracy
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