2,632 research outputs found
RE-MOVE: An Adaptive Policy Design Approach for Dynamic Environments via Language-Based Feedback
Reinforcement learning-based policies for continuous control robotic
navigation tasks often fail to adapt to changes in the environment during
real-time deployment, which may result in catastrophic failures. To address
this limitation, we propose a novel approach called RE-MOVE (\textbf{RE}quest
help and \textbf{MOVE} on), which uses language-based feedback to adjust
trained policies to real-time changes in the environment. In this work, we
enable the trained policy to decide \emph{when to ask for feedback} and
\emph{how to incorporate feedback into trained policies}. RE-MOVE incorporates
epistemic uncertainty to determine the optimal time to request feedback from
humans and uses language-based feedback for real-time adaptation. We perform
extensive synthetic and real-world evaluations to demonstrate the benefits of
our proposed approach in several test-time dynamic navigation scenarios. Our
approach enable robots to learn from human feedback and adapt to previously
unseen adversarial situations
Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning
Mobile agents that can leverage help from humans can potentially accomplish
more complex tasks than they could entirely on their own. We develop "Help,
Anna!" (HANNA), an interactive photo-realistic simulator in which an agent
fulfills object-finding tasks by requesting and interpreting natural
language-and-vision assistance. An agent solving tasks in a HANNA environment
can leverage simulated human assistants, called ANNA (Automatic Natural
Navigation Assistants), which, upon request, provide natural language and
visual instructions to direct the agent towards the goals. To address the HANNA
problem, we develop a memory-augmented neural agent that hierarchically models
multiple levels of decision-making, and an imitation learning algorithm that
teaches the agent to avoid repeating past mistakes while simultaneously
predicting its own chances of making future progress. Empirically, our approach
is able to ask for help more effectively than competitive baselines and, thus,
attains higher task success rate on both previously seen and previously unseen
environments. We publicly release code and data at
https://github.com/khanhptnk/hanna . A video demo is available at
https://youtu.be/18P94aaaLKg .Comment: In EMNLP 201
Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory
The role of robots in society keeps expanding, bringing with it the necessity
of interacting and communicating with humans. In order to keep such interaction
intuitive, we provide automatic wayfinding based on verbal navigational
instructions. Our first contribution is the creation of a large-scale dataset
with verbal navigation instructions. To this end, we have developed an
interactive visual navigation environment based on Google Street View; we
further design an annotation method to highlight mined anchor landmarks and
local directions between them in order to help annotators formulate typical,
human references to those. The annotation task was crowdsourced on the AMT
platform, to construct a new Talk2Nav dataset with routes. Our second
contribution is a new learning method. Inspired by spatial cognition research
on the mental conceptualization of navigational instructions, we introduce a
soft dual attention mechanism defined over the segmented language instructions
to jointly extract two partial instructions -- one for matching the next
upcoming visual landmark and the other for matching the local directions to the
next landmark. On the similar lines, we also introduce spatial memory scheme to
encode the local directional transitions. Our work takes advantage of the
advance in two lines of research: mental formalization of verbal navigational
instructions and training neural network agents for automatic way finding.
Extensive experiments show that our method significantly outperforms previous
navigation methods. For demo video, dataset and code, please refer to our
project page: https://www.trace.ethz.ch/publications/2019/talk2nav/index.htmlComment: 20 pages, 10 Figures, Demo Video:
https://people.ee.ethz.ch/~arunv/resources/talk2nav.mp
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